eDiscovery and Review

Self Service

A leading energy company gained the flexibility to use self-service technology and full-service expertise as needed, reducing costs and optimizing outcomes. Key Actions A multinational energy company sought eDiscovery efficiency and scalability A seamless combination of self-service Lighthouse Spectra eDiscovery and full-service Lighthouse consulting enabled them to meet a wide range of needs Minor matters can be addressed with low-cost self-service tools A full-service Lighthouse team applies in-depth review expertise to complex matters Key Results $50,000 year-over-year cost reduction 100+ hours freed for matter-critical work Flexibility to meet varying matter requirements Training improved speed and accuracy of self-service eDiscovery What They Needed A multinational energy company wanted to stop relying on an expensive patchwork of third-party eDiscovery providers and adopt a unified, cost-effective strategy. It sought transparent pricing and self-service access to the latest technology, including Relativity and Brainspace. At the same time, it needed a consistent team of experienced eDiscovery and review experts for more in-depth needs. How We Did It Lighthouse listened closely as the company described its desire for greater scalability and efficiency. We proposed a seamless combination of self-service capabilities on the Lighthouse Spectra platform and a dedicated full-service team for complex matters. This proven, flexible approach minimizes cost for minor matters while ensuring available capacity and expertise for complex projects. The Lighthouse Spectra support team accelerated onboarding through technical assistance and training. After completing a proof of concept, the client immediately began ingesting matters into Spectra. At the same time, we assembled a dedicated full-service team to be ready when needed. The Results Using the intuitive, familiar Lighthouse Spectra experience—incorporating Relativity and Brainspace functionality—the client rapidly discovered and reviewed data for internal investigations, subpoenas, and other minor matters. They no longer needed to license and manage Relativity and Brainspace separately, benefitting from a predictable, fixed-fee pricing model that fits their budget and scales to meet their needs. The Lighthouse team simplified data processing and exception handling, freeing resources to focus on strategic aspects of a given matter. As soon as a case warranted, they could triage it to the full-service team directly from the Spectra workspace. The result is a more responsive, cost-effective eDiscovery strategy, saving the company hundreds of hours and almost $50,000. Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; energy-industry; analyticsediscovery-review; client-success; lighting-the-path-to-better-ediscoveryCase-Study, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, energy-industry, analytics, ediscovery-review

Lighthouse goes beyond linear review to help a global technology company make its case to the IRS. Key Actions Targeting critical case documents with Key Document Identification rather than performing linear review on the whole document set. Identifying key events that took place within specific hours, by applying advanced linguistic modelling to overcome challenges presented by multiple time zones and different time stamp formats within email traffic. Key Results 1.5 million total documents reduced to roughly 37,500. Results in 100-500% less time and at 90-240% lower cost than linear review. Building a Case for Tax-Exempt Lunches A global technology company was facing IRS scrutiny over the complementary lunches the company provided to staff. Full-time workers were comped the meals because, the company claimed, staff were required to respond to emergencies during lunch hours. The IRS was dubious of that claim and inclined to consider the lunches a taxable benefit. To prevent the meals from being taxed, the company needed to demonstrate to the IRS that, over a two-year period, at least 50% of employees at its San Francisco office had in fact responded to an emergency between the hours of 11 a.m. and 2 p.m. local time. For evidence, the company had 1.5 million documents—mostly emails—pertaining to about 1,000 employees. The company reached out to Lighthouse for help finding the best case-building documents within those 1.5 million. Lighthouse offered its Key Document Identification service. Rather than prioritize documents for linear review, the Lighthouse team promised to identify the most valuable and evidential documents—and do so in less time and at a lower cost. Hacking Through the Haystack The Lighthouse team eliminated less-valuable documents in stages. First, they used an advanced algorithm to remove junk and duplicative documents, reducing the document set to 943,000 (a 38% reduction). Among those, the team targeted San Francisco employee names and emails, which brought the total down to 484,000 (an additional 49% reduction). From here, the team employed nuanced, multi-layered linguistic search techniques to zero in on the most necessary and informative documents. Along the way, Lighthouse encountered a number of challenges that would have thwarted other search tools and teams. One of these was the knot of different time stamps attached to emails: the last in time email in every thread was converted to Coordinated Universal Time (UTC), while every previous email in the thread was stamped according to the local time zone of the sender. The Lighthouse team circumvented this by searching the emails’ metadata, which converted all times to UTC. Using this metadata, the team was able to search using a single timeframe (6 to 9 p.m. UTC, corresponding with 11 a.m. to 2 p.m. Pacific). Another challenge was looping together all emails stemming from the same incident, so that Lighthouse could provide the company with a complete account of each emergency response (and avoid counting a given emergency more than once). The team did this by flagging one email tied to a specific emergency and using proprietary threading technology to propagate that flagging to all other emails associated with that emergency. Finally, the Lighthouse team had to classify documents by level of emergency, to help the company build the strongest case. The emergency level of some documents was already classified, thanks to a system installed by the company toward the end of the two years under investigation. But for the majority of documents, it was unknown. Lighthouse was able to classify them using advanced search features of proprietary technology, which identified key terms like “time-sensitive” and other ways emergencies were referenced in the document population. Major Savings and Critical Insights In only two weeks, a two-person team delivered on Lighthouse’s promise to help the company gather evidence, shrink the document population, and save time and money. Had the company tried to build a case with linear review instead, it would have taken up to 5 times longer and cost up to twice as much. Of the 1.5 million total documents, Lighthouse escalated approximately 37,500 (2.5% of the original dataset). To help with case building, the team sorted documents into three tiers of descending priority: employees responding to high-level emergencies during the lunch hour, employees responding to any level of emergency during the lunch hour, and employees responding to high-level emergencies at any time in the day. The Lighthouse team also normalized the metadata for all documents to make it easy for company counsel to see which employees were involved in each document and thread. Across the three tiers: 78% of San Francisco employees were tied to at least one document 74% were tied to at least one non-propagated document (i.e., an email associated with a unique emergency) 68% were the sender of at least one non-propagated document This strongly suggested that more than 50% of employees actively responded to emergencies in the target timeframe and helped counsel hit the ground running in collecting the facts to prove it. Corporate Case Studycase-study; corporate; corporation; ediscovery; fact-finding; document-review; investigations; kdi; key-document-identification; keyword-search; tech-industry; analyticsediscovery-review; ai-and-analytics; client-successCase-Study, Corporate, Corporation, eDiscovery, fact-finding, document-review, investigations, KDI, key-document-identification, keyword-search, tech-industry, analytics, ediscovery-review, ai-and-analytics

Spectra, Lighthouse's cloud-based eDiscovery software, saved a pharmaceutical company cost by managing eDiscovery for a third-party subpoena in-house. What They Needed Faced with yet another third-party subpoena, a large pharmaceutical company started to question how they could address these types of matters in a more cost-effective manner. Although sometimes larger in terms of data volume, these types of matters aren’t generally complex and commonly don’t require the expertise and oversight of an outside vendor to manage the eDiscovery process. This case, in particular, had a large data volume with a low dollar value, so the company wanted to explore options outside of the traditional vendor and outside counsel review and production process. How They Did It Lighthouse had been exploring the idea of Spectra, our cloud-based, user-driven eDiscovery solution, with this client for some time and this third-party subpoena seemed to be the perfect fit for their first run. Although the matter was a bit larger in nature, with over 150 GBs of email, it could easily be self-driven by the client’s in-house team of experts within the Spectra environment. To begin, the Spectra team onboarded the client’s team into the tool and provided training, documentation, and access. From there, the client kicked off the matter and uploaded all the documents into Nuix to be processed with the click of a button. Nuix then quickly processed this data and loaded the resulting documents into Relativity for review. Upon investigation of the resulting ~750K document set, the client decided that instead of taking the time to craft and test search terms to identify the potentially relevant files, they preferred to engage Lighthouse’s Focus Discovery team to further reduce and refine the files needing to be reviewed. As a first step, all documents were run through Brainspace to flag lesser included emails that could be removed from the review. Out of the 771,825 documents loaded to Relativity, 168,628 (or 22% of the population), were able to be removed from the review entirely. Next, the client sent Lighthouse’s Focus Discovery team a request for production as well as the subpoena to aid in the search term creation and optimization process. The Focus group worked with the client to create and then optimize the search terms until only ~5,000 hits (0.6% of promoted docs) were flagged for review. At this point, the client team was able to organize the review and review the documents to ensure privilege was considered. Finally, the ~250 relevant documents were produced inside of Spectra and delivered for service to the other side. ‍ The Results Overall, the client was not only able to save significant money on linear review due to a reduced data volume, but also on the traditional review process, as they did not have to outsource it and instead could run their matter in one easy-to-use solution, while accessing on-demand expertise of the Focus Discovery team. The experience thus far has been overwhelmingly positive and the client now has an easy-to-use, self-service solution for handling third-party subpoenas (and other similar matters) in a more cost-effective manner. ‍ ‍ Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; analytics; pharma; ai-and-analytics; processingediscovery-review; ai-and-analytics; client-successCase-Study, client-success, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, analytics, Pharma, ai-and-analytics, analytics, Processing, ediscovery-review, ai-and-analytics

Lighthouse welcomes Ty Dedmon, Partner and lead of Bradley’s healthcare litigation team, to assess how generative AI is impacting litigation and what we can do to minimize the risk., Although the novel and often comical uses of generative AI have captured more recent headlines—think philosophical conversations with a chatbot or essays written in seconds using AI—there are big changes happening across sectors of the economy thanks to adoption of new tools and programs, including the legal and healthcare spaces. Recent case law and legislation highlights the new landscape emerging in healthcare litigation with potential long-term implications. Lighthouse welcomes Ty Dedmon , Partner at Bradley who leads their healthcare litigation team, to assess how generative AI is impacting litigation and what we can do to prepare, and to share advice on leverage AI innovation while minimizing the risk. This episode’s sighing of radical brilliance: “ Top AI companies agree to work together toward transparency and safety ,” Kevin Collier, NBCNews , July 21, 2023. Learn more about the show and our speakers on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and Twitter . , ai-and-analytics; ediscovery-review; information-governance, AI, analytics, eDiscovery, Review, information governance, generative AI, PHI, PII, healthcare, HIPAA, podcast, ai-and-analytics; analytics; artificial-intelligence; compliance; data-privacy; healthcare; healthcare-litigation; hipaa-phi; phi; pii; podcast; regulation

Lighthouse’s Brooks Thompson, Executive Director of Spectra, provides use cases for scaling and diversifying your eDiscovery platform and technology., As the demands of modern data, litigation, investigations, and data privacy continue to grow in scale and complexity, solutions for them need to adapt accordingly. Although there is a lot of noise around the latest generative AI promises or capabilities for eDiscovery, often legal teams and counsel merely need solutions that can effectively scale to their matters at hand. Deploying platforms or technology intended only for larger or more specific matters can be cumbersome and drain resources, leaving teams ill equipped for the variety of projects they encounter. Lighthouse’s Brooks Thompson , Executive Director of Spectra Operations and Support, joins the podcast to provide some practical advice and use cases for scaling and diversifying your eDiscovery platform and technology to make them more comprehensive. This episode’s sighing of radical brilliance: “ Why Companies Can — and Should — Recommit to DEI in the Wake of the SCOTUS Decision , ”Tina Opie and Ella F. Washington, Harvard Business Review , July 27, 2023. Learn more about the show and our speakers on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and Twitter . , ediscovery-review, eDiscovery, Review, , ediscovery; ediscovery-process; analytics; big-data; ai-and-analytics

Law & Candor welcomes Michael Bohner, Managing Discovery Attorney at Cleary, and Justin Van Alstyne, Head of Discovery and Information Governance at T-Mobile, to explore the practical aspects of this partnership, including balancing responsibilities, employing technology, and building relationships., In demanding and highly contentious litigation or investigations it can often feel like it‚Äôs every person for themselves without much room for partnership. However, this is a lost opportunity. The relationship between the strong trio of corporate counsel, law firms, and legal technology providers is often an unacknowledged key to overcoming critical challenges. By sharing key information, balancing workloads, and building on each other‚Äôs expertise, these partners can work together to solve modern data challenges and the toughest matters. Law & Candor welcomes Michael Bohner , Managing Discovery Attorney at Cleary, and Justin Van Alstyne , Head of Discovery and Information Governance at T-Mobile, to explore the practical aspects of this partnership, including balancing responsibilities, employing technology, and building relationships. This episode‚Äôs sighing of radical brilliance: ‚Äú Meet Aleph Alpha, Europe‚Äôs Answer to Open AI ,‚Äù Morgan Meaker, Wired, August 30, 2023.   Learn more about the show and our speakers on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and Twitter . , legal-operations; ediscovery-review, legal operations, eDiscovery, Review, corporate-legal-ops; ediscovery; law-firm; legal-ops; legal; corporate; ediscovery-process

Law & Candor welcomes Vanessa Quaciari, Senior eDiscovery Counsel at Baker Botts, to discuss improvements in collection, review, and production that can help you manage collaboration data.,   We are all participating in the unprecedented evolution of workplace communication. From virtually editing a shared document, to ‚Äúliking‚Äù a chat message, to responding to a colleague with an emoji during a video call‚Äîmost employees in a modern work environment are actively (and often unknowingly) creating large volumes of collaboration data. For the legal and eDiscovery professions, the speed of this innovation has necessitated parallel rapid advancements in technology and new approaches to workflows to stay ahead of the complexity and scale of chat and collaboration data. Law & Candor welcomes Vanessa Quaciari , Senior eDiscovery Counsel at Baker Botts, to discuss improvements in collection, review, and production that can help you manage collaboration data and scale your approach as the evolution continues. This episode's sighting of radical brilliance: ChatGPT If you enjoyed the show, learn more about our speakers and subscribe on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and  Twitter .  , chat-and-collaboration-data; ediscovery-review, collections, review, emerging data sources, podcast, production, chat-and-collaboration-data, ediscovery-review, collections; review; emerging-data-sources; podcast; production

Minimizing Re-Review

Cassie Blum, Senior Director of Review Consulting at Lighthouse, discusses how to implement a data reuse strategy, including what technology and workflows can optimize its success.,   Handling large volumes of data during an investigation or litigation can be anxiety-inducing for legal teams. Corporate datasets can become a minefield of sensitive, privileged, and proprietary information that legal teams must identify as quickly as possible in order to mitigate risk. Ironically, corporate data also provides a key to speeding up and improving this process. By reusing metadata and work product from past matters in combination with advanced analytics, organizations can significantly reduce risk and increase efficiency during the review process. Law & Candor welcomes Cassie Blum , Senior Director of Review Consulting at Lighthouse, to discuss how to implement this data strategy, including what technology and workflows can optimize its success. This episode's sighting of radical brilliance:  7 Ways to be a more inclusive colleague ,  Fast Company , February 24, 2023. If you enjoyed the show, learn more about our speakers and subscribe on lawandcandor.com , rate us wherever you get your podcasts, and join in the conversation on LinkedIn and  Twitter . , chat-and-collaboration-data; ediscovery-review; lighting-the-path-to-better-ediscovery, podcast, data reuse, document review, chat-and-collaboration-data, ediscovery-review, podcast; data-reuse; document-review

AI & Analytics

Law & Candor welcomes Kara Ricupero, Associate General Counsel at eBay, for a conversation about how analytics and reimagining review can help solve data challenges and advance business imperatives., In episode two, we introduce our new co-host Paige Hunt , Vice President of Global Discovery Solutions at Lighthouse, who will be joining Bill Mariano as our guide through the legal technology revolution. In their first Sighting of Radical Brilliance together they chat about an article in Wired that explores the rise of the AI meme machine, DALL-E Mini . Then, Paige and Bill interview Kara Ricupero , Associate General Counsel and Head of Global Information Governance, eDiscovery, and Legal Analytics at eBay. They explore how a dynamic combination of new technology and human expertise is helping to usher in new approaches to review and analytics that can help tackle modern data challenges. Other questions they dive into, include: How did you identify the kind of advanced technology needed for modern data challenges?   Partnering with the right people and experts across the business to utilize technology and insights seems to be a big part of the equation. How did you work with other stakeholders to leverage analytics?  With new analytics and intelligence, has it changed how you approach review on matters or other processes? How do you think utilizing analytics will evolve as data and review continue to change? What kinds of problems do you think it can help solve?  If you enjoyed the show, learn more about our speakers and subscribe on the  podcast homepage , listen and rate the show wherever you get your podcasts, and join in the conversation on  Twitter .  , ai-and-analytics; ediscovery-review; lighting-the-way-for-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscovery, review, data-re-use, ai/big data, podcast, ai-and-analytics, ediscovery-review, review; data-re-use; ai-big-data; podcast

Self Service

Our hosts chat with Justin Van Alstyne, Senior Corporate Counsel at T-Mobile, about best practices for handling internal investigations including the self service tools that have been most effective., Paige and Bill start the show with new and exciting research from MIT Sloan on artificial intelligence and machine learning.  Next, their interview with  Justin Van Alstyne , Senior Corporate Counsel, Discovery and Information Governance at T-Mobile. They dive into internal investigations, including how a simple, on-demand software solution can offer the scalability and flexibility teams need to manage investigations with varying amounts of data. Some other questions they explore are: How we collaborate and work has changed immensely over the past few years and that evolution doesn‚Äôt appear to be slowing down. How have new tools and data sources complicated conducting internal investigations?  With organizations encountering investigations of different sizes and degree, what workflows or approaches have you found are most flexible to respond to this variability? Along with process, technology is another key part of the equation. When choosing the right technology for internal investigations, what are some of your high-priority considerations? Are there any features that are must-haves? For people contemplating deploying a self service solution, what advice do you give to ensure your team has the right level of expertise and technology to handle their internal investigations at scale? If you enjoyed the show, learn more about our speakers and subscribe on the  podcast homepage , rate us wherever you get your podcasts, and join in the conversation on  Twitter .  , ediscovery-review; ai-and-analytics; lighting-the-path-to-better-ediscovery, self-service, spectra, podcast, ediscovery-and-review, ai-and-analytics, self-service, spectra; podcast

Megan Ferraro, Associate General Counsel, eDiscovery & Information Governance at Meta, joins Law & Candor to discuss the pivotal role legal is playing in helping innovation thrive while managing risk., Co-hosts Bill Mariano and Rob Hellewell start the show with Sightings of Radical Brilliance. In this episode, they review an article in Reuters exploring lawyer attrition and the ‚Äúgreat resignation.‚Äù Next, their interview with Megan Ferraro , Associate General Counsel, eDiscovery & Information Governance, Meta. They discuss the delicate balance that must be struck between risk and innovation and explore some of the following questions: How did the legal function evolve to play a bigger role in corporate strategy and innovation? What are the broader trends in the ways legal teams are supporting innovation? With businesses growing, adding new technology, and pivoting strategy quickly, what are the most critical risk challenges legal teams face today? How can legal best work with other functions in an organization to ensure strategic priorities are advanced‚Äîthrough new deals or technology, for example‚Äîwhile also balancing the risk factors?  Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us wherever you get your podcasts, and join in the conversation on Twitter .  Related Links   Blog post: Analytics and Predictive Coding Technology for Corporate Attorneys: Six Use Cases Podcast: Innovating the Legal Operations Model Blog post: What Skills Do Lawyers Need to Excel in a New Era of Business? Blog post: Purchasing AI for eDiscovery: Tips and Best Practices Article: To stem lawyer attrition, law firms must look beyond cash - report , ai-and-analytics; legal-operations; ediscovery-review, podcast, project management, risk management, ai-and-analytics, legal-operations, ediscovery-review,, podcast; project-management; risk-management

Our hosts and Harsha Kurpad of Latham Watkins discuss how to stay apprised of changes in AI technology in the ediscovery space and practical applications for more advanced analytics tools., Co-hosts Bill Mariano and Rob Hellewell start the show with Sightings of Radical Brilliance. In this episode, they review a recent  New York Times article by Cade Metz that explores how new organizations are using AI to find bias in AI . Next, they bring on Harsha Kurpad of Latham Watkins who answers the following questions around staying ahead of AI innovation in legal technology: What are some current barriers to adopting AI? How do you stay apprised of new AI technology, tools, and solutions? What are new data challenges that are leading to a greater adoption of AI or requiring the use of more sophisticated tools? How are government entities like the FTC and DOJ changing how AI is being used and what is required during investigations?  What are some best practices for training algorithms and staying on top of new approaches to training? What are some of the risks in not adopting AI or not staying apprised of changes to the tools, platforms, and how it‚Äôs being used. Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us on Apple and Stitcher , and join in the conversation on Twitter . Related Links White Paper: The Challenge with Big Data Blog Post: What Attorneys Should Know About Advanced AI in eDiscovery: A Brief Discussion Podcast: AI and Analytics for Corporations: Common Use Cases Blog Post: What is the Future of TAR in eDiscovery? (Spoiler Alert ‚Äì It Involves Advanced AI and Expert Services) , ai-and-analytics; ediscovery-review, privilege, review, ai/big data, tar/predictive coding, podcast, production, ai-and-analytics, ediscovery-review, privilege; review; ai-big-data; tar-predictive-coding; podcast; production

In this episode, Amanda Jones of Lighthouse will illuminate some common challenges and pitfalls that can arise with modern language in ediscovery., In the very first episode of season eight, co-hosts Bill Mariano and Rob Hellewell  introduce themselves and welcome listeners back for another riveting season of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution. They start off with some exciting news about Lighthouse and the recent acquisition of H5 . They then dive into Sightings of Radical Brilliance, the part of the show highlighting the latest news of noteworthy innovation and acts of sheer genius. In this episode, they discuss an article in the AP that investigates how AI-powered tech landed a man in jail with scant evidence . Bill and Rob discuss the case and the AI technology involved, and what questions this raises regarding scientifically validating AI and its use as evidence in criminal cases. Bill and Rob are then joined by Amanda Jones of Lighthouse to discuss common challenges and pitfalls that can arise with modern language in ediscovery, and the interplay between AI and linguistics. Some key questions they explore, include: What is linguistic modeling? What are the critical challenges with modern language and ediscovery today? How is linguistics informing and impacting AI in ediscovery? What are best practices for implementing AI solutions and tools? Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us on Apple and Stitcher , and join in the conversation on Twitter . , ai-and-analytics; ediscovery-review, review, emerging data sources, ai/big data, podcast, ai-and-analytics, ediscovery-review, review; emerging-data-sources; ai-big-data; podcast

Pooja Lalwani of Lighthouse and our hosts discuss these two ediscovery review methodologies, and walk through the advantages and disadvantages of both and which better supports AI technology., Bill Mariano and Rob Hellewell kick off this episode with another segment of Sightings of Radical Brilliance, where they discuss Dalvin Brown’s piece in the Washington Post about how AI was used to recreate actor Val Kilmer’s voice . Bill and Rob consider this great scientific achievement along with the potentially nefarious ways it can used. Next, our hosts chat with Pooja Lalwani of Lighthouse about two key approaches to ediscovery review: family and four corner. Pooja helps break down the benefits and drawbacks of each through questions such as: What are some of the key differences between both approaches? With modern communication platforms and data creating a more dynamic and complex review process, what are some of the considerations for when and how to deploy family and four corner review? What review methodology is better suited to supporting TAR and AI tools? How do these review methodologies either help classify privilege more efficiently or potentially create limitations? Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us on Apple and Stitcher , and join in the conversation on Twitter . , ediscovery-review; ai-and-analytics; lighting-the-way-for-review; lighting-the-path-to-better-review, privilege, review, ai/big data, tar/predictive coding, podcast, ediscovery-review, ai-and-analytics, privilege; review; ai-big-data; tar-predictive-coding; podcast

Bill and Rob bring on Jason Rylander of Axinn to discuss techniques for unifying matter data across an organization's portfolio and how it can save significant time and money on document review., Join co-hosts Bill Mariano and Rob Hellewell as they discuss a law firm that only works on artificial intelligence and whether this is an emerging trend for the industry. Next, they‚Äôre joined by Jason Rylander of Axinn to discuss the antitrust landscape, benefits of cross-matter review, and techniques for unifying matter data across an organization‚Äôs portfolio. Jason and our hosts walk through key questions, including: With a new administration and the continued disruption from COVID, has there been an increase in the volume of antitrust matters, investigations, and litigation? What are some of the challenges or disadvantages of doing the traditional single-matter document review? What are some strategies for identifying work product or data that can be reused or repurposed?  What are some best practices when connecting matters?  Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, learn more about our speakers and subscribe on the podcast homepage , rate us on Apple and Stitcher , and join in the conversation on Twitter . , ediscovery-review; ai-and-analytics, collections, tar/predictive coding, hsr second requests, processing, podcast, data reuse, project management, ediscovery-review, ai-and-analytics, collections; tar-predictive-coding; hsr-second-requests; processing; podcast; data-reuse; project-management

This episode is a recording of the Law & Candor Live eDiscovery Day webinar. In this episode, co-hosts Bill Mariano and Rob Hellewell kick things off by discussing the first-ever AI Santa Claus in...,   This episode is a recording of the Law & Candor Live eDiscovery Day webinar. In this episode, co-hosts Bill Mariano and Rob Hellewell kick things off by discussing the first-ever AI Santa Claus in the midst of COVID-19. The co-hosts then bring on Zach Warren of Legaltech News, Ryan O'Leary of IDC and Chris Dahl of Lighthouse to answer to following questions: What are the top trends of 2020? How do you overcome common challenges of key trends in 2020? With these trends in mind, how do you prepare for 2021? Will the pandemic have lasting effects on the ediscovery space? Will this permanently affect how data collections are done? How to prepare for RIFs? Subscribe to the show here , rate us on Apple and Stitcher, connect with us on Twitter , and discover more about our speakers and the show here . , ediscovery-review, emerging data sources, data-privacy, ai/big data, podcast, ediscovery-review,, emerging-data-sources; data-privacy; ai-big-data; podcast

Microsoft‚Äôs agile development and rapid product enhancement allows Microsoft 365 (M365) users to stay up to date with emerging industry challenges. However, keeping pace with these M365 features,   In the final episode of season five, co-hosts  Bill Mariano and  Rob Hellewell review an article on a recent ILTA>ON panel that examined how  tech has created certain power dynamics in legal space. Next, Bill and Rob bring on John Collins of Lighthouse to walk them through the top M365 features to leverage in an ediscovery program. Together they cover the latest and greatest as well as uncover answers to the following questions:  How many updates and enhancements is Microsoft making? How often/fast are these coming out? What are some of the common challenges around these rapid changes?  What are the top M365 features that folks in the industry should be aware of? Are there other ways and/or resources folks can use to stay up-to-date? The season ends with key takeaways from the guest speaker section. Subscribe to the show here , rate us on Apple and Stitcher, connect with us  Twitter , and discover more about our speakers and the show  here . Related Links Blog Post: Microsoft 365, G-Suite, and the Growing Demand for Consulting and ifying Experts Blog Post: Leveraging Microsoft 365 to Reduce Your eDiscovery Spend Blog Post: Key Compliance & Information Governance Considerations As You Adopt Microsoft Teams Podcast Episode:  Microsoft Office 365 Part 1: Microsoft‚Äôs Influence on the Next Evolution of eDiscovery Podcast Episode: Microsoft Office 365 Part 2: How to Leverage all the Tools in the Toolbox   , microsoft-365; ediscovery-review; chat-and-collaboration-data, microsoft, podcast, microsoft-365, ediscovery-review, chat-and-collaboration-data,, microsoft; podcast

Being able to scale an ediscovery program from a self-service to a full-service model for particular matters can save both time and money, thus allowing for a more efficient ediscovery program overall,   In the second episode of season five, co-hosts  Bill Mariano and  Rob Hellewell kick off the show with Sightings of Radical Brilliance. In this episode, they discuss  Solos Health Analytics‚Äôs new technology (FeverGaurd) that was designed as a fever detection software to stop the spread of COVID-19 and the PPI challenges it could raise.  Next, they bring on  Claire Caruso of Lighthouse. Together, the three of them talk through how to scale ediscovery programs from self-service to full-service and back through the following questions:  When would one need to transition from self service to full service, and back to self service?  What are the benefits of making these moves? What are some of the key things to look out for?  What are some recommendations for folks looking to optimize their structure? Our co-hosts wrap up the episode with a few key takeaways. If you enjoyed the show, subscribe here , rate us on Apple and Stitcher, join in the conversation on  Twitter , and discover more about our speakers and the show  here . Related Links Blog Post:  How to Bring eDiscovery In House from Seasoned Self-Service Adopters Podcast Episode:  The Future of On-Demand SaaS Software for Small Matters ‚Äì A Self-Service Model Story Blog Post:  Overcoming  Top Objections for Moving to a Self-Service eDiscovery Model Blog Post:  Building a Business Case for Upgrading Your eDiscovery Self-Service Practices in Six Simple Steps Podcast Episode:  Moving to the Cloud Part 1: A Corporate Journey Podcast Episode:  Moving to the Cloud Part 2: A Law Firm Journey About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click  here .   , ediscovery-review; ai-and-analytics, self-service, spectra, podcast, ediscovery-review, ai-and-analytics, self-service, spectra; podcast

In the second episode of season four, co-hosts¬†Bill Mariano and¬†Rob Hellewell kick off the show with¬†Sightings of Radical Brilliance. In this episode, they discuss¬†how the¬†U.S. House plans to...,   In the second episode of season four, co-hosts  Bill Mariano and  Rob Hellewell kick off the show with Sightings of Radical Brilliance. In this episode, they discuss how the  U.S. House plans to start voting remotely and the impacts this could have on the legal space.  They then introduce the next guest speaker segment, which features  Tracy Hallenberger of Baker Botts. They unravel the myths behind managed services and discuss the key benefits of this modern approach to ediscovery through the following questions:  What are some of the top myths that are associated with managed services? What about this myth around lesser quality? What about the myth around it being more expensive? What about this lower service level to lawyer myth? What are the key benefits of a managed services model? Our co-hosts wrap up the episode with a few key takeaways. Join in the conversation on  Twitter and discover more about our speakers and the show  here . Related Links Case Study:  Lighthouse‚Äôs Managed Service Solution Delivers More Than $13 Million in Savings over Six Years Case Study:  Top Ten Global Law Firm Realizes BeneÔ¨Åts of Lighthouse Managed Services About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click  here .   , ediscovery-review, managed services, podcast, ediscovery-review,, managed-services; podcast

Law & Candor co-hosts¬†Bill Mariano and¬†Rob Hellewell kick things off with¬†Sightings of Radical Brilliance, in which they discuss¬†how¬†password dumping can improve your security and what that means...,   Law & Candor co-hosts  Bill Mariano and  Rob Hellewell kick things off with Sightings of Radical Brilliance, in which they discuss how  password dumping can improve your security and what that means for the future of security.  In this episode, Bill and Rob are joined by  Dave Kuhl of Lighthouse. The three uncover the complexities around managing cybersecurity as well as practical tips for overcoming challenges via the following questions: What are the recent complexities around managing cybersecurity? What are today‚Äôs biggest threats? What are some key lessons learned around these challenges? How do you combat cybersecurity challenges? How do you get ahead of these issues before they hit? In conclusion, our co-hosts end the episode with key takeaways. To join the conversation, connect with us  Twitter and discover more about our speakers and the show  here . Related Links Blog Post: Cybersecurity in eDiscovery: Protecting Your Data from Preservation through Production Blog Post: Top Three Tips for Structuring an Effective eDiscovery Security Evaluation Podcast Episode:  Cybersecurity in eDiscovery: Protecting Your Data from Preservation through Production Webinar Recording: The Risks of Cybersecurity in eDiscovery ‚Äì Is Your Data Safe? About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click  here .   , data-privacy; ediscovery-review; information-governance, cybersecurity, podcast, data-privacy, ediscovery-review, information-governance,, cybersecurity; podcast

In the fourth episode of season four, co-hosts¬†Bill Mariano and¬†Rob Hellewell discuss the¬†first-ever trial by Zoom, how it all went down, as well as what may expect to see looking forward.¬†Bill...,   In the fourth episode of season four, co-hosts  Bill Mariano and  Rob Hellewell discuss the  first-ever trial by Zoom , how it all went down, as well as what may expect to see looking forward.  Bill and Rob then introduce their guest speaker,  Zander Brandt of Lyft, who shares his experience as a two-time corporate ediscovery ‚Äúfirst employee‚Äù and what it takes to set up an effective and efficient ediscovery program. Zander answers the following questions in this episode: What is that like being the first corporate ediscovery employee? Where do you start in a role like this? What are the key initial steps to take when coming on board? What are things to avoid? Common pitfalls? What are the recommendations/best practices for those looking to implement an efficient ediscovery program today? Our co-hosts wrap up the episode with a few key takeaways. Follow us on  Twitter and discover more about our speakers and the show  here . About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click  here .   , ediscovery-review; legal-operations, ediscovery process, legal ops, podcast, ediscovery-review, legal-operations, ediscovery-process; legal-ops; podcast

In this special edition of Law & Candor, co-hosts¬†Bill Mariano and¬†Rob Hellewell, kick things off with¬†Sightings of Radical Brilliance, the part of the show where they discuss the latest news of...,   In this special edition of Law & Candor, co-hosts  Bill Mariano and  Rob Hellewell , kick things off with Sightings of Radical Brilliance, the part of the show where they discuss the latest news of noteworthy innovation and acts of sheer genius. Within this episode, they discuss the recent innovative trend around large car manufactures switching gears around their production plans in the midst of COVID-19 to help  develop ventilators and  supply masks to help fight the pandemic. Related to COVID-19, the guest speaker segment of the show features Lighthouse‚Äôs CEO, Brian McManus, who shares his take on the industry impacts of COVID-19. The trio cover current top company priorities, common themes being heard throughout the industry, as well as the lasting impacts of this pandemic on the legal space by answering the following key questions: What are key company priorities? What are current employee safety priorities and items to be aware of? What is the industry saying? What will be the lasting impact of COVID-19 on the legal space?  In conclusion, they share top takeaways from the episode. If you enjoyed the show, join in the conversation on  Twitter and discover more about our speakers and the show  here . Related Links Webinar Recording: Top Tips for Staying Productive and Connected While Working from Home  About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click  here .   , ediscovery-review, ediscovery process, podcast, ediscovery-review,, ediscovery-process; podcast

Discover how data repositories can be set up to reuse data for future matters in this podcast episode.,   In the second episode of season one, co-hosts Bill Mariano and Rob Hellewell kick off the show with SIGHTINGS OF RADICAL BRILLIANCE. In this episode, they discuss the company Big Moon Power and some of the exciting things they are doing to harness the power of ocean tides to generate electricity. Next, they introduce their guest Erika Namnath , Executive Director of Advisory Services at Lighthouse, to discuss the truth behind data reuse. Together, they uncover the answers to the questions below: What is data reuse? What are the different types of data reuse? How would you reuse data around trade secrets and IP? What about privilege, PII, and PHI? What are some of the current limitations that companies are facing when trying to leverage data reuse? What about objectively non-responsive documents? How do you handle those types of work product for data reuse? What are the key benefits of data reuse? Finally, our co-hosts wrap up the episode with a few key takeaways. Join in on the conversation on Twitter and discover more about our speakers and the show here . About Law & Candor Law & Candor is a podcast wholly devoted to pursuing the legal technology revolution. Co-hosts Bill Mariano and Rob Hellewell explore the impacts and possibilities that new technology is creating by streamlining workflows for ediscovery, compliance, and information governance. To learn more about the show and our speakers, click here . , ediscovery-review, ediscovery-and-review, data-re-use; podcast

The first step to moving beyond the AI hype is also the most important. That’s when you ask: How can AI actually make my work better? It’s the great ROI question. Technology solutions are only as good as the benefits they provide. So it’s critical to consider an AI solution’s efficacy—its ability to deliver the benefits you expect of it—before bringing it onboard.To help you do that, let’s walk through what efficacy means in general, and then look at what it means for the two types of modern AI.Efficacy varies depending on the solutionYou can measure efficacy in whatever terms matter most to you. For simplicity’s sake, let’s focus on quality, speed, and cost.When you’re looking to improve efficacy in those ways, it’s important to remember that not all AI is the same. You need to choose technology suited for your task. The two types of AI that use large language models (LLMs) are predictive AI and generative AI (for a detailed breakdown, see our previous article on LLMs and the types of AI). Because they perform different functions, they impact quality, speed, and cost in different ways.Measuring the efficacy of predictive AIPredictive AI predicts things, such as the likelihood that a document is responsive, privileged, etc. Here’s how it works, using privilege as an example. Attorneys review and code a sample set of documents.Those docs are fed to the AI model to train it—essentially, teaching it what does and doesn’t count as privilege for this matter.Then, the classifier analyzes the rest of the dataset and assigns a percentage to each document: The higher the percentage, the more likely the document is to be privileged.The training period is a critical part of the efficacy equation. It requires an initial investment in eyes-on review, but it sets the AI up to help you reduce eyes-on review down the line. The value is clearest in large matters: Having attorneys review 4,000 documents during the training period is more than worth it when AI removes more than 100,000 from privilege review.With that in mind, here’s how you could measure the efficacy of a predictive AI priv classifier.Quality: Does AI make effective predictions? AI privilege classifiers can be very effective at identifying privilege, including catching documents that other methods miss. A client in one real-life matter used our classifier in combination with search terms—and our classifier found 1,600 privileged docs that weren’t caught by search terms. Without the classifier, the client would have faced painful disclosures and clawbacks.Speed: Does predictive AI help you move faster? AI can accelerate review in multiple ways. Some legal teams use the percentages assigned by their AI priv classifier to prioritize review, starting with the most likely docs and reviewing the rest in descending order. Some use the percentages to cull the review population, removing docs below a certain percentage and reviewing only those docs that meet a certain threshold of likelihood. One of our clients often does both. For 1L review, they prioritize docs that score in the middle. Docs with extremely high or low percentages are culled: The most likely docs go straight to 2L review, while the least likely docs go straight to production. By using this method during a high-stakes Second Request, the client was able to remove 200,000 documents from privilege review.Cost: Does predictive AI save you money? Improving speed and quality can also improve your bottom line. During the Second Request mentioned above, our client saved 8,000 hours of attorney time and more than $1M during privilege review.Measuring the efficacy of generative AIGenerative AI (or “gen AI”) generates content, such as responses to questions or summaries of source content. Use cases for gen AI in eDiscovery vary widely—and so does the efficacy.For our first gen AI solution, we picked a use case where efficacy is straightforward: privilege logs. In this case, we’re not giving gen AI open-ended questions or a sprawling canvas. We’re asking it to draft something very specific, for a specific purpose. That makes the quality and value of its output easy to measure.This is another case where AI’s performance is tied to a training period, which makes efficacy more significant in larger matters. After analysts train the AI on a few thousand priv logs, the model can generate tens of thousands on its own.Given all that, here’s how you might measure efficacy for gen AI.Quality: Does gen AI faithfully generate what you’re asking it to? This is often tricky, as discussed in an earlier blog post about AI and accuracy in eDiscovery. Depending on the prompt or situation, gen AI can do what you ask it to without sticking to the facts. So for gen AI to deliver on quality and defensibility, you need a use case that affords: Control—AI analytics experts should be deeply involved, writing prompts and setting boundaries for the AI-generated content to ensure it fits the problem you’re solving for. Control is critical to drive quality.Validation—Attorneys should review and be able to edit all content generated by AI. Validation is critical to measure quality.Our gen AI priv log solution meets these criteria. AI experts guide the AI as it generates content, and attorneys approve or edit every log the AI generates. As a result, the solution reliably hits the mark. In fact, outside counsel has rated our AI-generated log lines better than log lines by first-level contract attorneys.Speed: Does gen AI help you move faster? If someone (or something) writes content for you, it’s usually going to save you time. But as I said above, you shouldn’t accept whatever AI generates for you. Consider it a first draft—one that a person needs to review before calling it final. But reviewing content is a lot faster than drafting it, so our priv log solution and other gen AI models can definitely save you time.Cost: Does gen AI save you money?Giving AI credit for cost savings can be hard with many use cases. If you use gen AI as a conversational search engine or case-strategy collaborator, how do you calculate its value in dollars and cents?But with priv logs, the financial ROI is easy to track: What do you spend on priv logs with gen AI vs. without? Many clients have found that using our gen AI for the first draft is cheaper than using attorneys.Where can AI be effective for you?This post started with one question—How can AI make your work better?—but you can’t answer it without also asking where. Where are you thinking about applying AI? Where could your team benefit the most?So much about efficacy depends on the use case. It determines which type of AI can deliver what you need. It dictates what to expect in terms of quality, speed, and cost, including how easy it is to measure those benefits and whether you can expect much benefit at all.If you’re struggling to figure out what benefits matter most to you and how AI might deliver on them, sign up to receive our simple guide to thinking about AI below. It walks through seven dimensions of AI that are changing eDiscovery, including sections on efficacy, ethics, job impacts, and more. Each section includes a brief questionnaire to help you clarify where you stand—and what you stand to gain.

Blogs Template M 3 Editing Share Publish From Data to Decisions, AI is Improving Accuracy for eDiscovery from-data-to-decisions-ai-is-improving-accuracy-for-ediscovery www.lighthouseglobal.com/blog/ from-data-to-decisions-ai-is-improving-accuracy-for-ediscovery 05/28/2024 From Data to Decisions, AI is Improving Accuracy for eDiscovery Learn through real scenarios how predictive and generative AI are helping to improve accuracy in eDiscovery. You’ve heard the claims that AI can increase the accuracy of analytical tasks during eDiscovery. They’re true when the AI in question is being developed responsibly through proper scoping, iterative testing, and validation. As we have known for over a decade in legal tech circles, the computational power of AI (and machine learning in particular) is perfectly suited to the large data volumes at play for many matters and the types of classification assessments required for document review. But how much of a difference can AI make? And what impact do large language models (LLMs) have in the equation beyond traditional approaches to machine learning? How do these boosts in accuracy help legal teams meet deadlines, preserve budget, and achieve other goals? To answer these questions, we’ll look at several examples of privilege review from real-world matters. Priv is far from the only area where AI can make a difference, but for this article it’ll help to keep a tight focus. Also, we’ve been enhancing privilege review with AI since 2019, so when it comes to accuracy—we have plenty of proof. What accuracy means for the two primary types of AI Before we explore examples, let’s review the two relevant categories of AI and what they do in an eDiscovery context. Predictive AI leverages historical data to predict outcomes on new data. For eDiscovery, we leverage predictive AI to provide us with a metric on the likelihood a document falls under a certain classification (responsive, privileged, etc.) based on a previously coded training set of documents. Generative AI creates novel content based directly on input data. For eDiscovery, one example could be leveraging generative AI to develop summaries of documents of interest and answers to questions we may have about the facts present in these documents. In today's context, both types of AI are built with LLMs, which learn from vast stores of information how to navigate the nuances and peculiarities of language as people actually write and speak it. (In a previous post, we share more information about LLMs and the two types of AI.) Because each of these types of AI are focused on different goals and have different outputs, predictive and generative AI also have different definitions of accuracy. Accuracy for predictive AI is tied to a traditional sense of the truth: How well can the model predict what is true about a given document? Accuracy for generative AI is more fluid: A generative AI model is accurate when it faithfully meets the requirements of whatever prompt it was given. If you ask it to tell you what happened in a matter based on the facts at hand, it may make up facts in order to be accurate to the prompt. Whether the response is true or is based on the facts of the matter depends on the prompt, tuning mechanisms, and validation. All that said, both types of AI have use cases that allow legal teams to measure their accuracy and impact. Priv classifiers prove to be more accurate than search terms Our first example comes from a quick-turn government investigation of a large healthcare company. For this matter, we worked with counsel to train an AI model to identify privilege and ran it in conjunction with privilege search terms. The privilege terms came back with 250K potentially privileged documents, but the AI model found that more than half of them (145K) were unlikely to be privileged. Attorneys reviewed a sample of the disputed docs and agreed with the AI. That gave counsel the confidence they needed to remove all 145K from privilege review—and save their client significant time and money. We saw similar results in another fast-paced matter. Search terms identified 90K potentially privileged documents. Outside counsel wanted to reduce that number to save time, and our AI privilege model did just that. Read the full story on AI and privilege review for details. Let’s return to our definition of accuracy for predictive AI: How well did the model predict what was true about the documents? Very well and more accurately than search terms. Now what about generative AI? Generative AI can draft more accurate priv logs than people We have begun to use generative AI to draft privilege log descriptions. That’s an area where defining accuracy is clear-cut: How well does the log explain why the doc is privileged? During the pilot phase of our AI priv log work, we partnered with a law firm to answer that very question. With permission from their client, the firm took privilege logs from a real matter and sent the corresponding documents through our AI solution. Counsel then compared the log lines created by our AI model against the original logs from the matter. They found that the AI log lines were 12% more accurate than those drafted by third party contract reviewers. They also judged the AI log lines to be more detailed and less repetitious. We have evidence from live matters as well. During one with a massive dataset and urgent timeline, outside counsel used our generative AI to create privilege logs and asked reviewers to QC them. During QC, half the log lines sailed through with zero edits, while the other half were adjusted only slightly. You can see what else AI achieved in the full case study about this matter. More accurate review = more efficient review (with less risk) Those accuracy numbers sound good—but what exactly do they mean for legal teams? What material benefits do you get from improving accuracy? Several, including: Better use of attorney and reviewer time. With AI accurately identifying priv and non-priv documents, attorneys spend less time reviewing no-brainers and more time on documents that require more nuanced analysis. In cases where every document will be reviewed regardless, you can optimize review time (and costs) by sending highly unlikely docs to lower-cost contract resources and reserving your higher-priced review teams for close calls. Opportunities for culling. Attorneys can choose a cutoff at a recall that makes sense for the matter (including even 100%) and automatically remove all documents under that threshold from review and straight into production. This is a crisp, no-fuss way to avoid spending time and resources on documents highly unlikely to be privileged. Lower risk of inadvertently producing privileged documents. Pretty simple: The better your system is for classifying privilege, the less likely you are to let privileged info slip through review. What does accuracy mean to you? I hope this post helps clarify how exactly AI can improve accuracy during eDiscovery and what other benefits that can lead to. Now it’s time to consider what all this means to you, your team, and your work. How important is accuracy to you? How do you measure it? Where would it help to improve accuracy, and what would you get out of that? To help you think it through, we assembled a user-friendly guide that covers accuracy and six other dimensions of AI that change the way people think about eDiscovery today. The guide includes brief definitions and examples, along with key questions like the ones above to help you craft an informed, personal point of view on AI’s potential.

AI, generative AI, antitrust, second requests, HSR, eDiscovery, review, information governance, healthcare, legal operations, law firm, corporate counsel ai-and-analytics; compliance; corporate; corporate-legal-ops; data-analytics; healthcare; healthcare-litigation; innovative-technology; innovation; information-governance; law-firm; mergers; modern-data; phi; pii; podcast; self-service, spectra; regulation; production mitch montoya In a year of unprecedented advancement in AI capabilities and economic uncertainty, legal teams and attorneys have been given both a compelling look into what the future of their work may look like and a sharp picture of today’s challenges. With a critical eye on how to manage and capitalize on these dueling perspectives that define legal’s current landscape, the guests on the new season of Law & Candor offer insights on a range of issues, including generative AI, new M&A guidelines and HSR rules, collaboration data, strategic partnerships, and the future of the industry. Listen for news, AI and technology updates, and best practices from leaders confronting these challenges and charting new paths forward. Episode 1: The Power of Three: Maximizing Success with Law Firms, Corporate Counsel, and Legal Technology Episode 2: What You Need to Know About the New FTC and DOJ HSR Changes Episode 3: Why Your eDiscovery Program and Technology Need Scalability Episode 4: Generative AI and Healthcare: A New Legal Landscape Episode 5: The Great Link Debate and the Future of Cloud Collaboration To keep up with news and updates on the podcast, follow Lighthouse on LinkedIn and Twitter . And check out previous episodes of Law & Candor at lighthouseglobal.com/law-and-candor-podcast. For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.

Similar matters often pull in the same documents for review during eDiscovery. Many legal teams default to manually reviewing these documents for each matter, but this is quickly becoming untenable.Legal teams can reduce the burden of repeated review through the application of advanced technology and proactive review strategies. They may encounter barriers, from limitations of their current tools to concerns about defensibility. But legal teams can take small steps now that overcome these barriers and prepare them to meet the time, budget, and other pressures they face today.Repeated review exacerbates today’s challengesWe're approaching a time when legal teams simply can’t afford to review the same documents multiple times across matters. The size of modern datasets requires teams to reduce eyes-on review wherever possible. Meanwhile, repeated review of the same documents opens the door to inconsistency, error, and risk.When teams succeed in reducing repeated review, they turn their most common pain points into new sources of value. They get more out of their review spend, help review teams work faster, and achieve the accuracy that they expect and the courts demand.In an earlier post, we dig more deeply into when repeated review happens, what it costs, and how technology can support a different approach. If you’re eager to explore solutions, that’s a great place to start.But many legal professionals are unable to think about solutions yet. They face a range of internal and external barriers that make it hard to move or even see past the status quo of repeated review.If that’s the boat you’re in, keep reading.Changing your approach may appear dauntingLegal teams often have solid reasons for persisting with repeated review. These include:Feasibility concerns Every matter is unique, and teams may assume this means nothing of value carries over from one matter to another.Attorneys may distrust the decisions or data practices associated with prior matters. They prefer starting over from scratch, even if it means repeating work.Technology barriers Legacy tools and software lack the advanced AI necessary to save work product and apply learnings from matter to matter, but adopting new technology takes time and money that legal teams are wary of spending.Companies who use multiple vendors and eDiscovery review teams may store their data in multiple places, making it hard to reuse past work product.It’s true that no two matters are exactly alike, adopting new technology can be challenging, and it’s hard to trust the reliability of something you’ve never used before. But this doesn’t mean that repeated review is still the best option. As shown above, the costs are simply too high.So, what do we do about these barriers? How are teams supposed to move past them? The answer: One step at a time.Explore what’s possible by starting smallThese barriers are most formidable when you imagine rethinking your entire review approach. The idea of looking for potential document overlap across a huge portfolio, or finding and implementing a whole new technology suite, may be too overwhelming to put into action.So don’t think of it that way. Take small steps that explore the potential for reducing repeated review and chip away at the barriers holding you back. Instead of “all or nothing,” think “test and learn.”Look ahead to future matters, perform hybrid QC on past decisionsTo explore the feasibility of reducing repeated review, look at one matter with an eye on overlap. Does it share fundamental topics or custodians with any recent or future matters? Is it likely to have spin-off litigations, such as cases in other jurisdictions or a civil suit that follows a federal one?To build trust in decisions made during prior matters, try performing QC with attorneys and technology working in tandem. This can provide a quick and informative assessment of past decisions and calibrate your parameters for review going forward.Find a technology partner who meets you where you areIf your team lacks the technology to reuse work product, the right partner can right-size a solution for your needs and appetite. The hybrid QC example above applies here too. Many legal teams find that QC is an ideal venue for assessing the performance of advanced AI and getting a taste for how it works, because it’s focused, confined, and accompanied by human reviewers. From there, your team might expand to using advanced AI on a single matter, and eventually, on multiple matters. In all cases, your partner can do the heavy lifting of operating the technology, while explaining each step along the way, with enough detail that you can articulate its use and merits in court (or can “tag in” to present that explanation for you). “The right partner” in this context is someone with the data science expertise to apply the technology in the ways you need, along with the legal experience to speak to your questions and need for defensibility.Likewise, when data or case work are spread across multiple teams and locations, a savvy partner can still find ways to avoid duplicate work. This story about coordinating review across 9 jurisdictions is a great example.Take your time—but do take actionThe beauty of starting small is how it respects both the need to improve and the difficulty of making improvements. Changing something as intricate and important as your document review strategy won’t happen overnight. That’s okay. Take your time. But don’t take repeated review as a given. It threatens timelines, budgets, and quality. And it’s not your only option.For more on the subject, including specific scenarios where teams can reduce repeated review, see our in-depth primer.ediscovery-review; ai-and-analyticsreview; ai-and-analytics; ediscovery; ediscovery-processsarah moran

Modern data volumes and complexity have ushered in a new era of document review. The traditional approach, in which paid attorneys manually review all or most documents in a corpus, fails to meet the intense needs of legal teams today.Specifically, legal teams need to:• Scale their document review capability to cover massive datasets• Rapidly build case strategy from key information hidden in those datasets• Manage the risk inherent in having sensitive and regulated information dispersed across those datasetsAdvanced review technology—including AI-powered search tools and analytics—enables teams to meet those needs, while simultaneously controlling costs and maintaining the highest standards of quality and defensibility. Rather than ceding decisions to a computer, reviewers are empowered to make faster decisions with fewer impediments. (For a breakdown of how technology sets reviewers up for success, see our recent blog post). In a nutshell, legal teams that use advanced technology can be more flexible, tackling large datasets with fewer resources, and addressing strategy and risk earlier in the process.A flexible approach to scale: refining the responsive setResponsiveness is the center of all matters. With datasets swelling to millions of documents, legal teams must reduce the responsive set defensibly, cost-efficiently, and in a way they can trust.With traditional eyes-on review, the only way to attempt this is to put more people or hours on the job. And this approach requires reviewers to make every coding decision, which is often mentally taxing and prone to error. Advanced review technology is purpose-built to scale for large datasets and provide a more nuanced assessment of responsiveness. Namely, it assigns a probability score—say, a given document is 90% or 45% likely to be responsive—that you can use to guide the review team. Often this means reviewers start with the highest-probability docs and then proceed through the rest, eventually making their way through the whole corpus. But legal teams have a lot of flexibility beyond that. Combining machine learning with rules-based linguistic models can make responsive sets vastly more precise, decreasing both risk and downstream review costs.Using this approach, machine learning is leveraged for what it does best—identifying clearly responsive and clearly non-responsive materials. For documents that fall in the middle of machine learning’s scoring band—those the model is least certain about—linguistic models built by experts target responsive language found in documents reviewed by humans, and then expand out to find documents with similar language markers. This approach allows legal teams to harness the strengths of both computational scalability and human reasoning to drive superior review outcomes.A flexible approach to strategy: finding key documents fasterOnly about 1% to 1.5% of a responsive set consists of key documents that are central to case planning and strategy. The earlier legal teams get their hands on those documents, the sooner they can start on that invaluable work.Whereas it takes months to find key documents with traditional review, advanced technology shortens the process to mere weeks. This is because key document identification utilizes complex search strings that include key language in context. For example, “Find documents with phrase A, in the vicinity of phrases B, C, and D, but not in documents that have attributes E and F,” and so on. A small team of linguistic experts drafts these searches and refines them as they go, based on feedback from counsel. In one recent matter, this approach to key document identification proved 8 times faster than manual review, and more than 90% of the documents it identified had been missed or discarded by the manual review team.The speed and iterative nature of this process are what enable legal teams to be more flexible with case strategy. First, they have more time to choose and change course. Second, they can guide the search team as their strategy evolves, ensuring they end up with exactly the documents they need to make the strongest case.A flexible approach to risk: assessing privilege and PII sooner and more cost effectivelyReviewing for privilege is a notoriously slow and expensive part of eDiscovery. When following a traditional approach, you can’t even start this chore until after the responsive set is established.With advanced technology, you can review for privilege, PII, and other classifications at the same time that the responsive set is being built. This shortens your overall timeframe and gives you more flexibility to prepare for litigation.Legal teams can even be flexible with their privilege review budget. As with responsiveness, advanced technology will rate how likely a document is to be privileged. Legal teams can choose to send extremely high- and low-scoring documents to less-expensive review teams, since those documents have the least ambiguity. Documents that score in the middle have the most ambiguity, so they can be reviewed by premium reviewers.It’s all about options in the endBroadly speaking, the main benefit of supporting document review with advanced technology is that it gives you a choice. Legal teams have the option to start key tasks sooner, calibrate the amount and level of eyes-on review, and strategize how they use their review budgets. With linear review, those options aren’t available. Legal teams that give themselves these options, by taking advantage of supportive technology, are better able to scale, strategize, and manage risk in the modern era of document review.ediscovery-review; ai-and-analyticsediscovery-review, ai-and-analytics, ai/big dataai-big-data eric pender

Handling large volumes of data during an investigation or litigation can be anxiety-inducing for legal teams. Corporate datasets can become a minefield of sensitive, privileged, and proprietary information that legal teams must identify as quickly as possible in order to mitigate risk. Ironically, corporate data also provides a key to speeding up and improving this process. By reusing metadata and work product from past matters in combination with advanced analytics, organizations can significantly reduce risk and increase efficiency during the review process.In a recent episode of Law & Candor, I discussed the complex nature of corporate data and ways in which the work done on past matters—coupled with analytics and advanced review tools—can be reused and leveraged to reduce risk and increase efficiency for current and future matters. Here are my key takeaways from the conversation.From burden to asset: leveraging data and analytics to gain the advantageThe evolution of analytical tools and technologies continues to change the data landscape for litigation and investigations. In complex matters especially—think multi-district litigation, second requests, large multi-year projects with multiple review streams—the technology and analytics that can now be applied to find responsive data not only helps streamline the review process but can extend corporate knowledge beyond a single matter for a larger purpose. Companies can now use their data to their advantage, transforming it from a liability into an asset. Prior to standardization around threading and TAR and CAL workflows, repository models were the norm. Re-use of issue coding was the best way to gain efficiency, but each matter still began with a clean slate. Now, with more sophisticated analytics, it’s not just coding and work product that can be re-used. The full analysis that went into making coding decisions can be applied to other matters so that the knowledge gained from a review and from the data itself is not lost as new matters come along. This results in greater overall efficiencies—not to mention major cost-savings—over time.Enhanced tools and analytics reduce the risk of PII, privilege, and other sensitive data exposureWith today’s data volumes, the more traditional methods used in review, such as search terms and regular expression (regex), can often result in high data recall with low precision. That is, such a wide net is cast that a lot of data is captured that isn’t terribly significant, and data that does matter can be missed. Analytical modeling can help avoid that pitfall by leveraging prior work product and coding to reduce the size of the data population from the outset, sometimes by as much as 90%, and to help find information that more traditional tools often miss.This is especially impactful when it comes to PII, PHI, and privileged or other sensitive data that may be in the population, because the risk of exposure is significantly reduced as accuracy increases. Upfront costs may seem like a barrier, but downstream cost savings in review make up for itWhen technology and data analytics are used to reduce data volume from the beginning, efficiencies are gained throughout the entire review process; there are exponential gains moving forward in terms of both speed and cost. Unfortunately, the upfront costs may seem steep to the uninitiated, presenting what is the likely barrier to the lack of wide adoption of many advanced technologies. The initial outlay before a project even begins can be perceived as a challenge for eDiscovery cost centers. Also, it can be very difficult for any company to keep up with the rapid evolution of both the complex data landscape and the analytics tools available to address it—the options can seem overwhelming. Finding the right technology partner with both expertise and experience in the appropriate analytics tools and workflows is crucial for making the transition to a more effective approach. A good partner should be able to understand the needs of your company and provide the necessary statistics to support and justify a change. A proof-of-concept exercise is a way to provide compelling evidence that any up-front expenditure will more than justify a revised workflow that will exponentially reduce costs of linear document review.How to get startedSeeing is believing, as they say, and the best way to demonstrate that something works is to see it in action. A proof-of-concept exercise with a real use case—run side-by-side with the existing process—is an effective way to highlight the efficiencies gained by applying the appropriate analytics tools in the right places. A good consulting partner, especially one familiar with the company’s data landscape, should be able to design such a test to show that the downstream cost savings will justify the up-front spend, not just for a single matter, but for other matters as well. Cross-matter analysis and analytics: the new frontierTAR and CAL workflows, which are finally finding wider use, should be the first line of exploration for companies not yet well-versed in how these workflows can optimize efficiency. But that is just the beginning. Advanced analytics tools add an additional level of robustness that can put those workflows into overdrive. Cross-matter analysis and analytics, for example, can address important questions: How can companies use the knowledge and work product gleaned from prior matters and apply them to current and future matters? How can such knowledge be pooled and leveraged, in conjunction with AI or other machine learning tools, to create models that will be applicable to future efforts?Marrying the old school data repository concept with new analytics tools is opening a new world of possibilities that we’re just beginning to explore. It’s a new frontier, and the most intrepid explorers will be the ones that reap the greatest benefits. For more information on data reuse and other review strategies, check out our review solutions page.ai-and-analytics; data-privacy; ediscovery-reviewcorporate; ai-and-analytics; analytics; big-data; compliance-and-investigations; corporationcassie blum

There is a common theme buzzing around the legal tech and eDiscovery industry – the Cloud and how in-house lawyers should be aware of the implications of their companies moving to the Cloud. Due to its regular appearance, there is an increasing focus on the legal implications of moving to the Cloud, rather than IT and operational considerations, within organisations.Setting the StageThe Cloud is familiar to most people thanks to the way we store photos and save emails. However, the impact of the Cloud in such a short space of time, even for personal users, is remarkable. Google now gives away cloud storage space worth around $15,000 per person at 1995 prices to its users (of which there are approximately 1 billion). In other words, what would have cost a combined $15 trillion just 24 years ago is now being offered for free (Goldin and Kutarna. Age of Discovery. 1990. Print.).The common response to the question of moving a companies' data to the Cloud is typically around perceived issues of both cost and security. Both of these topics are fundamental but are limited in scope when considering the wide-ranging potential of enterprise cloud technology from the perspective of data governance, compliance, and eDiscovery.Reducing or eliminating IT spend on building and maintaining infrastructure is a driving force for companies to move to the Cloud. Another is the need to provide employees with the tools they need to not only continue their everyday tasks but also to adapt and innovate. Microsoft recently quoted that, “97% of Fortune 500 and 95% of Fortune 1000 companies have Office 365 to benefit from streamlined infrastructure, data management, and collaborative technology opportunities.” They have discovered that cloud-based productivity has moved far beyond just standard applications like Word or Excel. Networked applications fuel employee innovation. According to a study by Vanson Bourne, “companies leveraging cloud services increased their time to market by 20.7%. At the same time, IT spending decreased by 15.1%, and, as for employees, productivity jumped 18.8%.”When compared to cost savings and data security, data governance, compliance, and eDiscovery often get less consideration. This is because a transition to the cloud is a core business decision, taken on at an enterprise-wide level to streamline the company and provide business-critical tools to employees. The legal capabilities of the technology may seem peripheral to the IT teams focusing on transitioning from on-premise infrastructure to cloud-based data centres. However, when you consider the variety of ways in which data is generated and the volume of this data, legal needs to lead the way in managing risk and adding value to how collaboration is managed across the company.Driving Home the PointIronically, cloud-based technologies like Office 365 make it even easier to generate ever-larger amounts of data. It is, therefore, no surprise that the same technology can (and should) be used to govern this data. Legal needs to consider how to take ownership of the companies' data for risk management purposes if nothing else.An example of this is persistent chat using Skype, Teams, Yammer, etc. Legal rather than IT needs to drive the key questions. Is this functionality available to everyone? How long is chat data stored? Does the company utilise more than one chat solution and do they interact with each other? Is the data discoverable if necessary and can it be searched? Can a legal hold be placed on this content? When deleted, does that fit with the overall data retention policy and is that consistent across multiple locations?Just one aspect of data governance that, of data retention and associated policies and logistics, can be overwhelming. Every organisation has many applications that employees use. A switch to a cloud-based environment doesn’t just mean the data is stored somewhere else. It means that tools are probably available for employees to work more intelligently and collaboratively. This is a positive thing for both efficiency and most likely profitability. It is also positive in terms of data governance and compliance. Policies such as data retention and categorisation can be refreshed so that they are not written and ignored. They can be hardwired into the very applications that generate the bulk of a company's data, from email and business documents to persistent chat applications, financial data, and internal social media.Cloud-based technology such as Office 365 can be utilised to manage contentious matters more effectively and proportionally (crucial for Subject Access Requests), without the need for large-scale intervention from third parties who deploy forensic data collection experts to ship large volumes of data elsewhere for eDiscovery purposes.Furthermore, failure to provide modern workplace technology often means that a shadow IT environment develops within a company, a phenomenon that makes governance and compliance even more difficult than it already is. Employees will use whatever technology they can to make their job easier, regardless of policy. Again, legal, not IT, can lead the way in aligning policies with the use of modern workplace tools.Fortunately, security concerns have done little to hold back the tide of progress to cloud-based infrastructure. Microsoft may be a company that has the most attempted external hacks, but it also has a budget of over $1 billion annually to ensure the data it holds is secure. Other cloud-based providers also understand the value of managing their clients’ data and have similar impressive ways and large budgets to protect it. Microsoft's share price demonstrates what shareholders think of their focus on the cloud over the last five years. Windows is not discussed as widely these days compared to Office 365.Looking Forward IT and security may be the departments responsible for a transition to the Cloud but legal and compliance are the departments that should take ownership of the generation and governance of the data. This should not be seen as a burden, but a welcome change in how to align a modern workplace with a comprehensive framework to manage risks inherent in big data.If you would like to discuss this topic further, please feel free to reach out to me at MBrown@lighthouseglobal.com.data-privacy; ediscovery-review; information-governance; microsoft-365cloud, information-governance, cloud-security, blog, data-privacy, ediscovery-review, information-governance, microsoft-365cloud; information-governance; cloud-security; blogmichael brown

The March sessions of Legalweek took place recently, and as with the February sessions, the virtual event struck a chord that reverberated deep from within the heart of a (hopefully) receding pandemic. However, the discussions this time around focused much less on the logistics of working in a virtual environment and much more on getting back to the business of law. One theme, in particular, stood out from those discussions – the idea that legal professionals will need to have a grasp on the technology that is driving our new world forward, post-pandemic.In other words, the days when attorneys somewhat-braggingly painted a picture of themselves as Luddites holed up in cobwebbed libraries are quickly coming to an end. We live in an increasingly digital world – one where our professional communications are taking place almost exclusively on digital platforms. That means each of us (and our organizations and law firms) are generating more data than we know what to do with. That trend will only grow in the future, and attorneys that are unwilling to accept that fact may find themselves entombed within those dusty libraries.Fortunately, despite our reputation as being slow to adapt, legal professionals are actually an innovative, flexible bunch. Whether a matter requires us to develop expertise in a specific area of the medical field, learn more about a niche topic in the construction industry, or delve into some esoteric insurance provision – we dive in and become laymen experts so that we can effectively advocate for our clients and companies. Thus, there is no doubt that we can and will evolve in a post-pandemic world. However, if anyone out there is still on the fence, below are four key reasons why attorneys will need to become tech savvy, or at least knowledgeable enough to understand when to call in technical expertise.1. Technological Competence is Imposed by Ethics and Evidence RulesFirst and foremost, attorneys have an ethical duty (under ABA Model Rule 1.1) to “keep abreast of changes in the law and its practice, including the benefits and risk associated with relevant technology.” Thirty seven states have adopted this language within their own attorney ethics rules. Thus, just as we have a duty to continue our legal education each year to stay abreast of changes in law, we also have an ethical duty to continue to educate ourselves on the technology that is relevant to our practice.We also have a duty to preserve and produce relevant electronically stored information (ESI) (under both the Federal Rules of Civil Procedure (FRCP), as well as the ABA model ethics rules)[1] during civil litigation. To do so, attorneys must understand (or work with someone who understands) where their client’s or company’s relevant ESI evidence is, how to preserve it, how to collect it, and how to produce it. This means preserving and producing not only the documents themselves but also the metadata (i.e., the information about the data itself, including when it was generated and edited, who created it, etc.). This overall process grows more complicated with each passing year, as companies migrate to the unlimited storage opportunities of the Cloud and employees increasingly communicate through cloud-based collaboration platforms. Working within the Cloud has a myriad of benefits, but it can make it more difficult for attorneys to understand where their client’s or company’s relevant information might be stored, as well as harder to ensure metadata is preserved correctly.Together, these rules and obligations mean that whether we are practicing law within a firm or as in-house counsel at an organization, we have a duty to understand the basics of the technology our clients are using to communicate so that at the very least, we will know when to call in technical experts to meet the ethical and legal obligations we owe to those we counsel.2. Data Protection and Data Privacy is Becoming Increasingly ImportantThe data privacy landscape is becoming a tapestry of conflicting laws and regulations in which companies are currently navigating as best they can. Within the United States alone, there were a multitude of state and local laws regulating personal data that came into effect or were introduced in 2020. For companies that have a global footprint, the worldwide data protection landscape is even more complicated – from the invalidation of the EU-US privacy shield to new laws and modifications of data protection laws across the Americas and Asia Pacific countries. It will not be long before most companies, no matter their location, will need to ensure that they are abiding within the constructs of multiple jurisdictional data privacy laws.This means that attorneys who represent those companies will need to understand not only where personal data is located within the company, but also how the company is processing that data, how (and if) that data is being transmitted across borders, when (and if) it needs to be deleted, the process for effectively deleting it, etc., etc. To do so, attorneys must also have at least some understanding of the technology platforms their companies and clients are using, as well as how data is stored and transferred within those platforms, to ensure they are not advertently running afoul of data privacy laws.As far as data protection, attorneys need to understand how to proactively protect and safeguard their clients’ data. There have been multiple high-profile data breaches in the last few months,and law firms and companies that routinely house personal data are often the target of those breaches. Protecting client data requires attorneys to have a semblance of understanding of where client data is and how to protect it properly, including knowing when and how to hire experts who can best offer the right level of protection.3. Internal Compliance is Becoming More Technologically Complicated There has been a lot of interest recently in using artificial intelligence (AI) and analytics technology to monitor internal compliance within companies. This is in part due to the massive amount of data that compliance teams now need to comb through to detect inappropriate or illegal employee conduct. From monitoring departing employees to ensure they aren’t walking out the door with valuable trade secret information, to monitoring digital interactions to ensure a safe work environment for all employees – companies are looking to leverage advances in technology to more quickly and accurately spot irregularities and anomalies within company data that may indicate employee malfeasance.Not only will this type of monitoring require an understanding of analytics and AI technology, but it will also require grasping the intricacies of the company’s data infrastructure. Compliance and legal teams will need to understand the technology platforms in place within their organization, where employees are creating data within those platforms, as well as how employees interact with each other within them.4. The Ability to Explain Technology Makes Us Better AdvocatesFinally, it is important to note that the ability to understand and explain the technology we are using makes us better and more effective advocates. For example, within the eDiscovery space, it can be incredibly important for our clients’ budgets and case outcomes to attain court acceptance of AI and machine-learning technology that can drastically limit the volume of data requiring expensive and tedious human review. To do so, attorneys often must first be able to get buy-in from their own clients, who may not be well versed in eDiscovery technology. Once clients are on-board, attorneys must then educate courts and opposing counsel about the technology in order to gain approval and acceptance.In other words, to prove that the methods we want to use (whether those methods relate to document preservation and collection, data protection, compliance workflows, or eDiscovery reviews) are defensible and repeatable, attorneys must be able to explain the technology behind those methods. And as in all areas of law, the most successful attorneys are ones who can take a very complicated, technical subject and break it down in a way that clients, opposing counsel, judges, and juries can understand (or alternatively are knowledgeable enough about the technology to know when it is necessary to bring experts in to help make their case).Best Practices for Staying Abreast of TechnologyReach out to technology providers to ask for training and tips when needed. When evaluating providers, look for those that offer ongoing training and support.For attorneys working as in-house counsel, work to build healthy partnerships with compliance, IT, and data privacy teams. Being able to ask questions and learn from each other will help head off technology issues for your company.For attorneys working within law firms, work to understand your clients’ data infrastructure or layout. This may mean talking to their IT, legal, and compliance teams so that you can ensure you are up to date on changes and processes that affect your ability to advocate effectively for your client.Look for CLEs, trainings, and vendor offerings that are specific to the technology you and your clients use regularly. Remember that cloud-based technology, in particular, changes and updates often. It is important to stay on top of the most recent changes to ensure you can effectively advocate for your clients.Recognize when you need help. Attorneys don’t need to be technological wizards in order to practice law, however, you will need to know when to call in experts…and that will require a baseline understanding of the technology at issue.To discuss this topic more, feel free to connect with me at smoran@lighthouseglobal.com. [1] ABA Model Rule 3.4, FRCP 37(e) and FRCP 26)ai-and-analytics; ediscovery-review; data-privacy; information-governanceanalytics, data-privacy, information-governance, ediscovery-process, blog, law-firm, ai-and-analytics, ediscovery-review, data-privacy, information-governanceanalytics; data-privacy; information-governance; ediscovery-process; blog; law-firmsarah moran

LegalWeek’s April conference took place recently, and as with the sessions earlier this year, the April thought leadership panels touched on many of the struggles we are all facing in the legal technology space. But where the February sessions focused on the post-pandemic future of legal technology and the March sessions focused on getting back to the business of law, the April sessions weaved in a more nuanced theme: obtaining organizational buy-in from stakeholders around legal technology and processes.The need for stakeholder buy-in for any type of legal technology change is imperative. Without it, organizations and law firms stop evolving and become stagnant as more agile competitors onboard better, more efficient processes, tools, and teams. But perhaps more importantly, being unable to obtain stakeholder involvement and approval can also end up leaving the company and law firms open to risk.For an example of the ramifications of failing to obtain the necessary buy-in, let’s take look at the legal technology process that many organizations and law firms have been struggling to implement recently: defensible disposal of legacy data. Without an effective defensible data disposal process and policy, data volumes can balloon out of control – especially in a Cloud environment – meaning that organizations and/or law firms will needlessly waste money storing obsolete data that should have been disposed of previously. But it also can increase risk in several ways. For starters, legacy data may contain personally identifiable information (PII) that organizations may be legally required to dispose of after a specified time period, pursuant to sectorial or jurisdictional data privacy laws. Even if personal data does not fall within the purview of a disposal requirement, keeping it for longer than it is needed for business purposes can still pose a risk should the company or firm holding it suffer a data breach or ransomware attack. Additionally, even obsolete non-personal data can cause confusion, disruption, and increased cost and risk if it winds up subject to a legal hold or swept up in an internal investigation. But despite all this, implementing an effective defensible data disposal program is a challenge for many because it often requires sweeping organizational buy-in, from the highest C-Suite executive to the lowliest employee with access to a company-sponsored collaborative platform.So how can legal teams get the buy-in necessary to implement new legal technology and processes that enable organizations and law firms to compete and evolve? It is tempting to think that buy-in starts with learning to control stakeholders. But attempting to control other teams and individuals will only lead to misalignment, tension, and failed implementation. Instead, gaining stakeholder buy-in actually starts with trust. Stakeholders must trust that whatever you are proposing to implement (whether that is a new technology, a new policy, or a new workflow) will be beneficial to them, to their team, and to the organization as a whole and that implementation is actually feasible. Below I have outlined a few tips for gaining stakeholder trust and buy-in for new legal technology and processes.Identify all the necessary stakeholders. Whether you want to onboard a new legal technology or implement a new legal data policy, like an updated document retention schedule, you will need to understand who the decisions makers are, as well as identify anyone who will be affected by the new tools, processes, or workflow.Prepare, Prepare, Prepare. Once you have identified the stakeholders and all those affected by the planned change, you can start preparing to gain their trust. This means doing all the necessary research and legwork up front so that you are well informed and have a fully developed, practical plan in place to present to those stakeholders. For instance, if you are seeking to onboard advanced AI technology to help streamline your eDiscovery program, you can prepare to gain trust by first talking to peers in the industry, as well as legal technology providers, to find the best technology and pricing options. Once you’ve selected an option, choose a test case and run a proof of concept to validate the effectiveness within your own data.Run the numbers. Once you’ve done the research and are satisfied that the new technology or workflow will be a good fit for your organization, quantify that fit by focusing on the bottom line. How much money will this be able to save your organization or law firm? How much risk can it eliminate and how can you quantify that risk? How can this new process or tool improve efficiency and how much money will that efficiency save? What is at stake if this new technology or process is not implemented and how can you quantify that? What is your plan for how this new tool or process will be funded by the organization or law firm?Stop, Collaborate, and Listen. Once you have identified all relevant stakeholders and collected the data, it is time to gather everyone together to present your research (either individually or via cross-organizational working groups or teams). Note that the order in which you present data to stakeholders will depend on your organization or law firm. For some, it may be best to get management and executives on board first to help drive change further downstream. In others, it may be more impactful to get lower-level teams on board first before presenting to final decision makers. Whichever order you choose, it is imperative to remember to listen and accept feedback once you’ve made your pitch. Remember this process will be iterative. It will require you to be flexible and possibly deviate from your original plan. It may also necessitate going back to the drawing board completely and selecting a different workflow or tool that works better for other groups. It may end up changing your desired implementation timeline. But the key to gaining trust from stakeholders is to get them involved early and listen to their feedback regarding planning, onboarding, and implementation.Retain Trust. Congratulations! Once all stakeholders have come to a consensus and you have achieved buy-in from all necessary decision makers, you are ready to implement and onboard. But that is not the end of this process. After implementation, you will need to protect the trust you have worked so hard to earn. You can do this by ensuring that everyone has the necessary training to effectively use the tool or abide by the new workflow or process. Nothing erodes trust more than incorrect (or non-existent) utilization. Whether you’re seeking to onboard a new eDiscovery platform or you’re rolling out a new legal hold technology, people who are affected by the change will need to understand how to use the technology and/or comply with the program. Set up training programs and then have avenues of ongoing support where people can ask questions and continue to train should they need it.I hope these tips come in handy when you are looking for buy-in from stakeholders around legal technology and processes. To discuss this topic more, feel free to connect with me at smoran@lighthouseglobal.com. ai-and-analytics; ediscovery-review; legal-operationscloud, data-privacy, information-governance, ai-big-data, preservation-and-collection, blog, ai-and-analytics, ediscovery-review, legal-operations,cloud; data-privacy; information-governance; ai-big-data; preservation-and-collection; blogsarah moran

However you view a DSAR, for any entity who receives one, they are time consuming to complete and disproportionately expensive to fulfill. Combined with the increasing manner in which they are being weaponized, companies are often missing opportunities to mitigate the negative effects of DSARs by not migrating data to the Cloud.Existing cloud solutions, such as M365 and Google Workplace (formerly known as G-Suite) allow administrators to,for example, set data retention policies, ensuring that data cannot routinely be deleted before a certain date, or that a decision is made as to when data should be deleted. Equally, legal hold functionality can ensure that data cannot be deleted at all. It is not uncommon for companies to discover that when they migrate to the Cloud all data is by default set to be on permanent legal hold. Whilst this may be required for some market sectors, it is worth re-assessing any existing legal hold policy regularly to prevent data volumes from ballooning out of control.Such functionality is invaluable in retaining data, but can have adverse effects in responding to DSARs, as it allows legacy or stale data to be included in any search of documents and inevitably inflates costs. Using built-in eDiscovery tools to search and filter data in place in combination with a data retention policy managed by multiple stakeholders (such as Legal, HR, IT, and Compliance) can mitigate the volumes of potentially responsive data, having a significant impact on downstream costs of fulfilling a DSAR.Typically, many key internal stakeholders are frequently unaware of the functionality available to their organization. This can help to mitigate costs, such as Advanced eDiscovery (AED) in Microsoft 365, or Google Vault in Google Workspace. Using AED, a user can quickly identify relevant data sources, from mailboxes, OneDrive, Teams, Skype, and other online data sources, apply filters such as date range and keywords, and establish the potential number of documents for review within in minutes. Compare this to those who have on-premise solutions, where they are wholly dependent on an internal IT resource, or even the individual data custodians, to identify all of the data sources, confirm with HR / Legal that they should be collected, and then either apply search criteria or export the data in its entirety to an external provider to be processed. This process can take days, if not weeks, when the clock is ticking to provide a response in 30 days. By leveraging cloud technology, it is possible to identify data sources and search in place in a fraction of the time it takes for on-premise data.Many cloud platforms include functionality, which means that when data is required for a DSAR, it can now be searched, filtered, and, crucially, reviewed in place. If required, redactions can be performed prior to any data being exported externally. Subject to the level of license held, additional functionality, such as advanced indexing or conceptual searching, can also be deployed, allowing for further filtering of data and thus reducing data volumes for review or export.The technology also allows for rapid identification of multiple data types including:Stale dataSensitive data types (financial information/ PII)Customer-specific dataSuspicious / unusual activitiesBy using the inbuilt functionality to minimize the impact of such data types as part of an Information Governance / Records Management program, there can be significant changes and improvements made elsewhere, including data retention policies, data loss prevention, and improved understanding of how data is routinely used and managed in general day-to-day business. This, in turn, has significant time and cost benefits when required to search for data, whether for a DSAR, investigation, or a litigation exercise. Subject to the agreement with the cloud service provider, this may also have benefits in reducing the overall volume and cost of data hosted.With a sufficiently robust internal protocol in place, likely data sources can be identified and mapped. Now, when a DSAR request is received, an established process exists to rapidly search and cull potential cloud-based data sources, including using tools such as Labels or Sensitivity Type to exclude data from the review pool, and efficiently respond to any such request.Migrating to the Cloud may seem daunting, but the benefits are there and can be best maximized when all stakeholders work together, across multiple teams and departments. DSARs do not have to be the burden they are today. Using tools readily available in the Cloud might also significantly reduce the burdens and costs of DSARs.To discuss this topic further, please feel free to reach out to me at MBicknell@lighthouseglobal.com.data-privacy; ediscovery-review; information-governance; microsoft-365cloud, dsars, cloud-services, blog, data-privacy, ediscovery-review, information-governance, microsoft-365cloud; dsars; cloud-services; blogmatt bicknell

KDI

Few things are more ominous to a company’s in-house counsel than the prospect of facing thousands of individual lawsuits across 30-40 jurisdictions, alongside various other companies in a multi-district litigation (MDL) proceeding. In-house teams can, of course, lean on the expertise of external law firms that have strong backgrounds in MDLs. However, even for experienced law firms, coordinating an individual company’s legal defense with other law firms and in-house counsel within a joint defense group (JDG) can be a Sisyphean task. But this coordination is integral to achieving the best possible outcome for each company, especially when it comes to identifying and sharing the documents that will drive the JDG’s litigation strategies. An MDL can involve millions of documents, emanating from multiple companies and their subsidiaries. Buried somewhere within that complicated web of data is a small number of key documents that tell the story of what actually happened—the documents that explain the “who, what, where, and when” of the litigation. Identifying those documents is critical so that JDG counsel can understand the role each company played (or didn’t play) in the plaintiffs’ allegations, and then craft and prepare their defense accordingly. And the faster those documents are identified and shared across a JDG, the better and more effective that defense strategy and preparation will be. In short: A strong and coordinated key document search strategy that is specific to the unique ecosystem of an MDL is crucial for an effective defense. Ineffective search strategies leave litigators out at sea Unfortunately, outdated or ineffective search methodologies are often still the norm rather than the exception. The two most common strategies were created to find key documents in smaller, insular litigation proceedings involving one company. They are also relics of a time when average data volumes involved in litigation were much smaller. Those two strategies are: one, relying on linear document review teams to surface key documents as they review documents one by one in preparation for production, and, two, relying on attorneys from the JDG’s counsel teams to arbitrarily search datasets using whatever search terms they think may be effective. Let’s take a deeper look at each of these methodologies and why they are both ineffective and expensive: Relying on linear review teams to find key documents. Traditional linear review teams are often made up of dozens or even hundreds of contract attorneys with no coordination around key document searches and little or no day-to-day communication with JDG counsel. Each attorney reviewer may also only see a tiny fraction of the entire dataset and have a skewed view of what documents are truly important to the JDG’s strategy. The results are often both overinclusive (with thousands of routine documents labeled “key” or “hot” that JDG counsel must wade through) and underinclusive (with truly important documents left unflagged and unnoticed by review teams). This search method is also painfully slow. Key documents are only incidentally surfaced by the review team if they notice them while performing their primary responsibility—responsive review. Relying on attorneys from JDG counsel teams. Relying on individual attorneys from the JDG’s outside counsel to perform keyword searches to find key documents is also ineffective and wastefully expensive. Without a very specific, coordinated search plan, attorneys are left running whatever searches each thinks might be effective. This strategy inevitably will risk plaintiffs finding critical documents first, leaving defense deposition witnesses unprepared and susceptible to ambush. This search methodology is also a dysfunctional use of attorney time and legal spend. Merits counsel’s value is their legal analytic skillset—i.e., their ability to craft the best litigation strategy with the evidence at hand. Most attorneys are not technologists or linguistic experts. Asking highly skilled attorneys to craft the most effective technological and linguistic data search is a bit like asking an award-winning sushi chef to jump onboard a fishing vessel, navigate to the best fishing spot, select the best bait, and reel in the fish the chef will ultimately serve. Both jobs require a highly specialized skillset and are essential to the end goal of delighting a client with an excellent meal. But paying the chef to perform the fisherman’s job would be ineffective and a waste of the chef’s skillset and time. Both of these search strategies are also reactive rather than proactive, which drives up legal costs, wastes valuable resources, and worsens outcomes for each company in a JDG. A better approach to MDL preparation and strategy Fortunately, there is a more proactive, cost-efficient, holistic, and effective way to identify the key documents in an MDL environment. It involves engaging a small team of highly trained linguists and technology search experts, who can leverage purpose-built technology to find the best documents to prepare effective litigation strategies across the entire MDL data landscape. A specialized team with this makeup provides a number of key advantages: Precise searches and results—Linguistic experts can carefully craft narrow searches that consider the nuance of human language to more effectively find key documents. A specialized search team can also employ thematic search strategies across every jurisdiction. This provides counsel with a critical high-level overview of the evidence that lies within the data for each litigation, enabling each company to make better, more informed decisions much earlier in the process.Quick access to key documents—Technology experts leveraging advanced AI and analytics can ensure potentially damaging documents bubble up to the surface—even in the absence of specific requests from JDG counsel. Compare this to waiting for those documents to be found by contract attorneys as they review an endless stream of documents, one by one, during the linear review process. A flexible offensive and defensive litigation strategy—A team of this size and composition can react more nimbly, circulate information faster, and respond quicker to changes in litigation strategy. For example, once counsel has an overview of the important facts, the search team can begin to narrow their focus to arm counsel with the data needed for both offensive and defensive litigation strategies. The team will be incredibly adept at analyzing incoming data provided by opposing counsel—flagging any gaps and raising potential deposition targets. Defensively, they can be used by counsel to get ahead of any potentially damaging evidence and identify every document that bolsters potential defense arguments. An expert partner throughout the process—A centralized search team is able to act as a coordinated “search desk” for all involved counsel, as well as a repository and “source of truth” for institutional knowledge across every jurisdiction. As litigation progresses, the search team becomes the right hand of counsel—using their knowledge and expertise to prepare deposition and witness preparation binders and performing ad-hoc searches for counsel. Once a matter goes to trial in one jurisdiction, the search team can use the information gleaned from that proceeding to inform their searches and strategy for the next case. Conclusion Facing a complex MDL is an undoubtedly daunting process for any company. But successfully navigating this challenge will be downright impossible if counsel is unable to quickly find and understand the key facts and issues that lie buried within massive volumes of data. Traditional key document search methodologies are no longer effective at providing that information to counsel. For a better outcome, companies should look for small, specialized search teams, made up of linguistic and technology experts. These teams will be able to build a scalable and effective search strategy tailormade for the unique data ecosystem of a large MDL—thereby proactively providing counsel with the evidence needed to achieve the best possible outcome for each company. lighting-the-way-for-review; ai-and-analytics; ediscovery-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscoveryreview, blog, ai, ai-and-analytics, ediscovery-reviewreview; blog; aikdisarah moran

The theme at the last CLOC conference was all about how the legal function is going through a tremendous evolution. Businesses are changing rapidly through digital transformation and remote or hybrid work environments while trying to capture the attention of technology saturated consumers. To remain competitive, legal departments must evolve to handle new types of work and constantly advancing processes and technologies, and consider how the legal function impacts the broader organization. They need to do this while also showing that their own department is embracing change, staying up on technology, and becoming more efficient. To do this well, legal department heads and the lawyers and professionals in the department will have to learn, and practice, some new skills: embracing technology, project management, change management, and adaptability. Some good news—recent trends in the legal space are helping departments and professionals facilitate and adapt to these changes. The first is an uptick in legal technologies available to legal departments. Instead of adapting to whatever technology the business makes available to the department, there are technologies built by lawyers for running a legal department. This trend means that lawyers have already started down the path of being more technology-forward. Second, the advent of the legal operations role—putting business discipline and rigor around the functioning of the legal department— has brought more robust project management and change management into many law departments. With these foundational blocks in place, lawyers must evolve their skills to take their department to the next level.The first, and likely most obvious, skill an attorney needs in a rapidly evolving business environment is a firm grasp on existing and emerging technology. There are two important categories of technology to consider—the first is legal technology and the second is broader technology trends. Legal technology not only facilitates the day-to-day functioning of the legal department—with e-billing, contract management, and project intake and workflow software—but also includes more complex categories such as eDiscovery and data management. To learn more about these technologies you can attend CLEs about relevant technologies in your area of practice or attend a legal technology conference. Outside of the legal space, there are also many general technology trends that are important for lawyers to be immersed in, including digital transformation, artificial intelligence, and digital payments and cryptocurrency. Digital transformation is all about changing from a brick and mortar, paper-based business to one that strategically leverages technology, digital tools, and the cloud to do the work. This is important for lawyers because it impacts the way their organizations contract and manage these technologies. Migrating to the cloud also benefits lawyers because it provides new technologies to manage legal departments.[1] Like cloud, AI has the ability to transform how lawyers work (e.g., check out our recent blog post on utilizing chatbots) as well as how their companies work. For both AI and digital transformation, reading and watching videos for IT leaders can help—although made for a different audience, there are lots of resources out there and they can provide the information relevant to lawyers. Finally, the plethora of digital payment methods and the volatility of cryptocurrency will have legal impacts in the future and lawyers should learn to understand the differences.The next set of skills is about project execution and management. As businesses change through digital transformation, it is equally important to transform the way legal departments work. To do that, learning effective business case presentation, project management, and change management are incredibly valuable talents. While diving into a full 30-page business case can sometimes be necessary, focusing time on learning to create an executive summary business case is time better spent for lawyers. You can find resources and templates in many places, including SmartSheet and Asana.There is a whole discipline around project management as well as multiple ways to drive results most effectively. Whether you take an agile approach or a more traditional method, the following skills are necessary: Cross-functional collaboration, including understanding and empathy for other departments, and influencing othersCommunication, including how to communicate effectively with a remote team – a reality that is often the norm in today’s worldTime management and prioritizationLeadership – leading a team and inspiring a team, and keeping team members engaged and focused both in the same office or working remotelyFacilitating a learning mindset across the project and team – ensuring that people are looking out for ways to continuously improve, learning from each step of the project, and iterating on each phase of the projectA couple of good resources for developing these skills include PMI.org, and LinkedIn Learning courses such as Project Leadership, Project Management Foundations: Communication, and Project Management Tips. Note that this is a discipline that can take years to perfect so focus on getting familiar with the concepts and then look for ways to get real life experience in your business. The best way to master these skills is through practice.While project management focuses on the process where you create a change, change management is a separate set of skills focused on moving people through that change. There are two components of change management lawyers need to know. The first is how to manage their own reaction to change—being adaptable can bring a lot of value to a volatile world.[2] Professor Anne Converse Willkom of Drexel University provides some great ways to work on becoming more adaptable here. The second part of change management is helping others through change. This may be your team or it could be a team impacted by a project you are leading. Harvard Business Review has a whole category of writing dedicated to this area, highlighting the importance of leading through change.There is a lot of information and resources to move through so it’s important to prioritize the areas and skills that will impact your role now and as you move through your career. From there, identify the list of resources you want to access to master those areas then work it in to your schedule. It’s important to budget 2-4 hours a week, at minimum, building your skills in one of these areas. If that seems like a lot, keep in mind that it is only 5-10% of a standard work week.‍[1] You can find more information on what this change is in this article by CIO.[2] It is sometimes hard to judge adaptability because we tend to be surrounded by like-minded thinkers. As such, relying on a third party resource can help. There is a great Forbes article that shares the signs of an adaptable person. Evaluate yourself versus this list and work on areas where you may not be adaptable.ediscovery-review; legal-operationsediscovery-process, blog, project-management, ediscovery-review, legal-operationsediscovery-process; blog; project-managementlighthouse

Since the dawn of modern litigation, attorneys have grappled with finding the most efficient and strategic method of producing discovery. However, the shift to computers and electronically stored information (ESI) within organizations since the 1990s exponentially complicated that process. Rather than sifting through filing cabinets and boxes, litigation teams suddenly found themselves looking to technology to help them review and produce large volumes of ESI pulled from email accounts, hard drives, and more recently, cloud storage. In effect, because technology changed the way people communicated, the legal industry was forced to change its discovery process.The Rise of TARDue to growing data volumes in the mid-2000s, the process of large teams of attorneys looking at electronic documents one-by-one was becoming infeasible. Forward-thinking attorneys again looked to technology to help make the process more practical and efficient – specifically, to a subset of artificial intelligence (AI) technology called “machine learning” that could help predict the responsiveness of documents. This process of using machine learning to score a dataset according to the likelihood of responsiveness to minimize the amount of human review became known as technology assisted review (TAR).TAR proved invaluable because machine learning algorithms’ classification of documents enabled attorneys to prioritize important documents for human review and, in some cases, avoid reviewing large portions of documents. With the original form of TAR, a small number of highly trained subject matter experts review and code a randomly selected group of documents, which are then used to train the computer. Once trained, the computer can score all the documents in the dataset according to the likelihood of responsiveness. Using statistical measures, a cutoff point is determined, below which the remaining documents do not require human review because they are deemed statistically non-responsive to the discovery request.Eventually, a second iteration of TAR was developed. Known as TAR 2.0, this second iteration is based on the same supervised machine learning technology as the riginal TAR (now known as TAR 1.0) – but rather than the simple learning of TAR 1.0, TAR 2.0 utilizes a process to continuously learn from reviewer decisions. This eliminates the need for highly trained subject matter experts to train the system with a control set of documents at the outset of the matter. TAR 2.0 workflows can help sort and prioritize documents as reviewers code, constantly funneling the most responsive to the top for review.Modern Data ChallengesBut while both TAR 1.0 and TAR 2.0 are still widely used in eDiscovery today – the data landscape looks drastically different than it did when TAR first made its debut. Smartphones, social media applications, ephemeral messaging systems, and cloud-based collaboration platforms, for example, did not exist twenty years ago but are all commonly used within organizations for communication today. This new technology generates vast amounts of complicated data that, in turn, must be collected and analyzed during litigations and investigations.Aside from the new variety of data, the volume and velocity of modern data is also significantly different than it was twenty years ago. For instance, the amount of data generated, captured, copied, and consumed worldwide in 2010 was just two zettabytes. By 2020, that volume had grown to 64.2 zettabytes.[1]Despite this modern data revolution, litigation teams are still using the same machine learning technology to perform TAR as they did when it was first introduced over a decade ago – and that technology was already more than a decade old back then. TAR as it currently stands is not built for big data – the extremely large, varied, and complex modern datasets that attorneys must increasingly deal with when handling discovery requests. These simple AI systems cannot scale the way more advanced forms of AI can in order to tackle large datasets. They also lack the ability to take context, metadata, and modern language into account when making coding predictions. The snail pace of the evolution of TAR technology in the face of the lightning-fast evolution of modern data is quickly becoming a problem.The Future of TARThe solution to the challenge of modern data lies in updating TAR workflows to include a variety of more advanced AI technology, together with bringing on technology experts and linguistics to help wield them. To begin with, for TAR to remain effective in a modern data environment, it is necessary to incorporate tools that leverage more advanced subsets of AI, such as deep learning and natural language processing (NLP), into the TAR process. In contrast to simple machine learning (which can only analyze the text of a document), newer tools leveraging more advanced AI can analyze metadata, context, and even the sentiment of the language used within a document. Additionally, bringing in linguists and experienced technologists to expertly handle massive data volumes allows attorneys to focus on the actual substantive legal issues at hand, rather than attempting to become an eDiscovery Frankenstein (i.e., a lawyer + a data scientist + a technology expert + and a linguistic expert all rolled into one).This combination of advanced AI technology and expert service will enable litigation teams to reinvent data review to make it more feasible, effective, and manageable in a modern era. For example, because more advanced AI is capable of handling large data volumes and looking at documents from multiple dimensions, technology experts and attorneys can start working together to put a system in place to recycle data and past attorney work product from previous eDiscovery reviews. This type of “data reuse” can be especially helpful in tackling the traditionally more expensive and time-consuming aspects of eDiscovery reviews, like privilege and sensitive information identification and can also help remove large swaths of ROT (redundant, obsolete, or trivial data). When technology experts can leverage past data to train a more advanced AI tool, legal teams can immediately reduce the need for human review in the current case. In this way, this combination of advanced AI and expert service can reduce the endless “reinventing the wheel” that historically happens on each new matter.ConclusionThe same cycle that brought technology into the discovery process is again prompting a new change in eDiscovery. The way people communicate and the systems used to facilitate that communication at work are changing, and current TAR technology is not equipped to handle that change effectively. It’s time to start incorporating more modern AI technology and expert services into TAR workflows to make eDiscovery feasible in a modern era.To learn more about the advantages of leveraging advanced AI within TAR workflows, please download our white paper, entitled “TAR + Advanced AI: The Future is Now.” And to discuss this topic more, feel free to connect with me at smoran@lighthouseglobal.com. [1] “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025” https://www.statista.com/statistics/871513/worldwide-data-created/practical-applications-of-ai-in-ediscovery; ai-and-analytics; ediscovery-reviewai-big-data, tar-predictive-coding, ediscovery-process, prism, blog, data-reuse, ai-and-analytics, ediscovery-reviewai-big-data; tar-predictive-coding; ediscovery-process; prism; blog; data-reusesarah moran

Since Judge Andrew Peck’s ruling over nine years ago in Da Silva Moore v. Publicis Groupe & MSL Group, the use of Technology-Assisted Review (TAR) for managing review in eDiscovery has been court approved. Yet many lawyers and legal professionals still don’t use machine learning (which, for many, is synonymous with TAR) in litigation. In the eDiscovery Today 2021 State of the Industry report, only 31.1% of respondents said they use TAR in all or most of their cases; 32.8% of respondents said they use it in very few or none of their cases. So, why don’t more lawyers use TAR?Transparency and TAROne possible reason that lawyers avoid the use of TAR is that requesting parties often demand more transparency with a TAR process than they do with a process involving keyword search and manual review. Judge Peck (retired magistrate judge and now Senior Counsel with DLA Piper) stated in the eDiscovery Today State of the Industry report: “Part of the problem remains requesting parties that seek such extensive involvement in the process and overly complex verification that responding parties are discouraged from using TAR.”In the article Predictive Coding: Can It Get A Break?, author Gareth Evans, a partner at Redgrave, states: “Probably the greatest impediment to the use of predictive coding has been the argument that the party seeking to use it should agree to share its coding decisions on the documents used to train the predictive coding model, including providing to the opposing party the irrelevant documents in the training sets.”Lawyer training vs. “black box” technologyWhy do lawyers expect that they are entitled to more transparency with TAR? Perhaps a better question might be: why do they demand less transparency for keyword search and manual review? One reason might lie in the education and training that they receive to become lawyers. Many lawyers cut their teeth on the keyword search used for resources like Westlaw and Lexis. Consequently, keyword search is part of their experience and they feel comfortable using it.Those same lawyers see keyword search and manual review for discovery as an extension of what they learned in law school. But it’s not. Search (aka “information retrieval”) is an expertise. Effective keyword search for discovery purposes is an iterative process that requires testing and verification of the search result set and the discard pile to confirm that the scope of the search wasn’t too narrowly focused. The end goal is to construct a search with both high recall and high precision; to identify those documents potentially responsive to a production request without also capturing non-responsive information, which can significantly increase review costs. This is very different from the goal of identifying a handful of documents that can assist in a case precedents argument.With regard to TAR, many lawyers still see the technology as a “black box” that they don’t understand. So, when the other side proposes using TAR, they want a lot more transparency about the particular TAR process to be used. It’s simply human nature to ask more questions about things we don’t understand. But, truth be told, lawyers should probably be just as vigilant in seeking information about the opposing’s use of keyword search as they are when TAR is the approach being proposed.TAR technology in daily livesWhat many lawyers may not realize is that they’re already using the type of technology associated with TAR elsewhere in their lives — albeit with a different goal and lower stakes than in a legal case. TAR is based on a supervised machine learning algorithm, where the algorithm learns to deliver similar content based on human feedback. Choices we make in Amazon, Spotify, and Netflix influence what those platforms deliver to us as other choices we might want to see in terms of items to buy, songs to listen to or movies to watch. The process of “training” the algorithms that drive these platforms makes them more useful to us — just as the feedback we provide during a predictive coding process helps train the algorithm to identify documents most likely to be responsive to the case.ConclusionWhat should lawyers do when opposing counsel makes transparency demands regarding TAR processes to be used? Certainly, cooperation and discussion of the protocol as soon as possible — such as the Rule 26(f) “meet and confer” between the parties — can help everyone get “on the same page” about what information can or should be shared, no matter what approach is proposed.However, if the parties can’t reach an accord regarding TAR transparency, perhaps another case ruling by Judge Peck — Hyles v. New York City — can be instructive here, where Judge Peck cited Sedona Principle 6. This principle states: “Responding parties are best situated to evaluate the procedures, methodologies, and technologies appropriate for preserving and producing their own electronically stored information.” Ironically, in Hyles, the requesting party was trying to force the responding party to use TAR, but Judge Peck, despite being an acknowledged “judicial advocate for the use of TAR in appropriate cases” denied the requesting party’s motion in that case. Transparency demands from requesting parties shouldn’t deter you from realizing the potential efficiency gains and cost savings resulting from an effective TAR process.For more information on H5 Litigation Services, including review for production with the H5 unique TAR as a Service, click here.ediscovery-reviewediscovery-reviewblog; tar; litigation; technology-assisted-review; predictive-coding; ediscovery; machine-learningmitch montoya

“The world’s most valuable resource is no longer oil, but data.” That’s what The Economist said in a fascinating opinion piece in 2017 that really stuck with me. This bold statement now seems more prescient than ever, as digital data continues to explode in volume and the advent of the cloud is significantly expanding where that valuable data, or electronically stored information (ESI), lives. So how has the legal world, and particularly all of us in the eDiscovery realm, fared?While this data revolution developed, lawyers, as per usual, have been a bit slow to adapt. As we’ve grappled with how to manage the explosion of data and cloud storage has gone mainstream, new and advanced SaaS solutions tied to cloud-based technology have taken the rest of the world by storm. It was only a matter of time before corporate clients would get on board and make the move to the cloud as is evident with the large majority of corporations who have transitioned to Office 365.So as clients focus more and more on controlling their budgets and demanding more eDiscovery efficiency, shifting to modernized, cloud-based SaaS technology seems like a no-brainer for law firms. What’s not to love about immediately eliminating the inefficiencies and manual tasks that accompany traditional eDiscovery workflows and creating satisfied clients in the process?In my previous two blogs, I discussed the top reasons why SaaS, self-service, spectra eDiscovery is exactly the right solution and the way of the future for law firms, and also best practices for embracing the data revolution. In this blog, I wrap up my SaaS exploration and present the top things to consider when choosing the best and most versatile SaaS solution for your eDiscovery program.1. Quick to Onboard - Software in the eDiscovery space has had a notoriously rocky road as far as simplicity and user friendliness. Many iterations of self-service, spectra, on-prem software are too complicated and require training fit for advanced users only. Another missing piece of the puzzle has often been the lack of clear and consumable metrics on areas like billing, usage, ingestion, and processing stats which are the key to helping inform users to make better decisions. With the new generation of eDiscovery technology, lawyers and litigation support professionals would most benefit from choosing a SaaS, self-service, spectra tool that’s quick and easy to understand. Look for a tool where cases with multiple users can be easily managed across matters and locations, and where you can create, upload, and process matters quickly, all with a customizable reporting dashboard.2. Access to Industry Leading Tools - One of the biggest issues we’ve seen as eDiscovery software has evolved is the need for users of on-prem software to purchase, install, and maintain multiple tools and systems in order to have a comprehensive internal workflow that spans the EDRM. This is not only expensive, but time consuming and risky considering the security implications that come with holding client data on your own servers. With SaaS, it’s critical to choose a platform that will provide access to all of the industry leading tools from processing to analytics to production in one comprehensive tool that is purchased, maintained, and upgraded by the solution provider. Users will immediately see direct cost savings from not having to manage multiple systems themselves when they adopt this type of end-to-end SaaS solution.3. Full-Service Support - Another important consideration when selecting a self-service, spectra, SaaS tool is to choose a flexible solution provider who can scale up if your matter changes and you end up needing full-service support. While having a self-service, spectra tool allows for complete independence in key areas like processing and production, what happens when your matter gets much bigger than anticipated and the data is too unwieldy to handle in-house, or if your internal team simply needs to shift their focus to something else? In this case, it’s critical to partner with a solution provider who has solid and experienced client support teams that can jump in any time you need help in your self-service, spectra journey.4. Secure Infrastructure - Last but not least, in this age of data breaches and cybersecurity on the top of the list of concerns for law firms and corporate clients alike, make sure you fully vet any SaaS tool you’re considering by thoroughly researching the solution provider’s back-end infrastructure. Look for vendors who have a scalable architecture for data processing and automation that you’ll be able to take full advantage of while eliminating the overhead that comes with infrastructure development and management on your end. That infrastructure should come with the peace of mind of security certifications such as SOC 2 and ISO 27001. You can also eliminate the concern that often comes with the security of a public cloud by choosing a solution provider that hosts data within their own private cloud or within their own data centers.Ultimately, as the global economy continues to shift from traditional commodities and lands squarely on data as its main driver, there’s a world of opportunity ahead for the legal world and eDiscovery. With data already moved to the cloud for most companies and their focus shifted to reducing expenses and risk, eDiscovery and SaaS for law firms is a perfect fit.ediscovery-review; ai-and-analyticscloud, self-service, spectra, blog, ediscovery-review, ai-and-analyticscloud; self-service, spectra; bloglighthouse

AI & Analytics

What does Artificial Intelligence (AI) mean to you? In the non-legal space, AI has taken a prominent role, influencing almost every facet of our day-to-day life – from how we socialize, to our medical care, to how we eat, to what we wear, and even how we choose our partners.In the eDiscovery space, AI has played a much more discreet but nonetheless important role. Its limited adoption so far is due, in part, to the fact that the legal industry tends to be much more risk averse than other industries. The innate trust we have placed in more advanced forms of AI technology in the non-legal world to help guide our decision making has not carried over to eDiscovery – partly because attorneys often feel that they don’t have the requisite technological expertise to explain the results to opposing counsel or judges. The result: most attorneys performing eDiscovery tasks are either not using AI technology at all or are using AI technology that is generations older than the technology currently being used in other industries. All this despite the fact that attorneys facing discovery requests today must regularly analyze mountains of complicated data under tight deadlines.One of the most prominent roles AI currently plays in eDiscovery is within technology assisted review (TAR). TAR uses “supervised” machine learning algorithms to classify documents for responsiveness based on human input. This classification allows attorneys to prioritize the most important documents for human review and, often, reduce the number of documents that need to be reviewed by humans. TAR has proven to be especially helpful in HSR Second Requests and other matters with demanding deadlines. However, the simple machine learning technology behind TAR is already decades old and has not been updated, even as AI technology has significantly advanced. This older AI technology is quickly becoming incapable of handing modern datasets, which are infinitely more voluminous and complicated than they were even five years ago.Because the legal industry is slower to adopt more advanced AI technology, many attorneys have a muddled view of what advanced AI technology exists, how it works, and how that technology can assist attorneys in eDiscovery today. That confusion becomes a significant detriment to modern attorneys, who must start being more comfortable with adopting and utilizing the more advanced AI tools available today if they stand a chance overcoming the increasingly complicated data challenges in eDiscovery. This confusion behind AI can also lead to a vicious cycle that further slows down technology adoption in the legal space: attorneys who lack confidence in their ability to understand available AI technology subsequently resist adoption of that technology; that lack of adoption then puts them even further behind the technology learning curve as technology continues to evolve. This is where legal technology companies with dedicated technology services can help. A good legal technology company will have staff on hand whose entire job it is to evaluate new technology and test its application and accuracy within modern datasets. Thus, an attorney who has no interest in becoming a technology expert just needs to be proficient enough to know the type of tools that might fit their needs – the right technology vendor can do the rest. Technology experts can also step in to help provide detailed explanations of how the technology works to stakeholders, as well as verify the outcome to skeptical opposing counsel and judges. Moreover, a good technology provider can also supply expert resources to perform much of the day-to-day utilization of the tool. In essence, a good legal technology vendor can become a trusted part of any attorney team – allowing attorneys to remain focused on the substantive legal issues they are facing. With that in mind, it’s important to “demystify” some common AI concepts used within the eDiscovery space and explain the benefits more advanced forms of AI technology can provide within eDiscovery. Once comfortable with the information provided here, readers can take a deeper dive into the advantages of leveraging advanced AI within TAR workflows in our full white paper – “TAR + Advanced AI: The Future is Now.” Armed with this information, attorneys can begin a more thoughtful conversation with stakeholders and legal technology companies regarding how to move forward with more advanced AI technology within their own practice.Demystifying AI Jargon in eDiscoveryAt its most basic, AI refers to the science of making intelligent machines – ones that can perform tasks traditionally performed by human beings. Therefore, AI is a broad field that encompasses many subfields and branches. The most relevant to eDiscovery are machine learning, deep learning, and natural language processing (NLP). As noted above, the technology behind legacy TAR workflows is supervised machine learning. Supervised machine learning uses human input to mimic the way humans learn through algorithms that are trained to make classifications and predictions. In contrast, deep learning eliminates some of that human training by automating the feature extraction process, which enables it to tackle larger datasets. NLP is a separate branch of machine learning that can understand text in context (in effect, it can better understand language the way humans understand it).The difference between the AI technology in legacy TAR workflows and more advanced AI tools lies in the fact that advanced AI tools use a combination of AI subsets and branches (machine learning, deep learning, and NLP) rather than just the supervised machine learning used in TAR. Understanding the Benefits of Advanced AIThis combination of AI subsets and branches used in advanced AI tools provides additional capabilities that are increasingly necessary to tackle modern datasets. These tools not only utilize the statistical prediction that supervised machine learning produces (which enables traditional TAR workflows), but also include the language and contextual understanding that deep learning and NLP provide. Deep learning and NLP technology also enable more advanced tools to look at all angles of a document (including metadata, data source, recipients, etc.) when making a prediction, rather than relying solely on text. Taking all context into consideration is increasingly important, especially when making privilege predictions that lead to expensive attorney review if a document is flagged for privilege. For example, with traditional TAR, the word “judge” in the phrases, “I don’t think the judge will like this!” on an email thread between two attorneys and, “Don’t judge me!” on a chat thread with 60 people regarding a fantasy football league will be classified the same way – because statistically, there is not much difference between how the word “judge” is placed within both sentences. However, newer tools that combine supervised machine learning with deep learning and NLP can learn the context of when the word “judge” is used as a noun (i.e., an adjudicator in a court of law) within an email thread with a small number of recipients versus when the word is being used as a verb on an informal chat thread with many recipients. The context of the data source and how words are used matters, and an advanced AI tool that leverages a combination of technologies can better understand that context.Using Advanced AI with TAROne common misconception regarding using newer, more advanced AI tools is that old workflows and models must go out the window. This is simply not true. While there may be some changes to review workflows due to the added efficiency generated by advanced AI tools (the ability to conduct privilege analysis simultaneously with responsive analysis, for example), attorneys can still use the traditional TAR 1.0 and TAR 2.0 workflows they are familiar with in combination with more advanced AI tools. Attorneys can still direct subject matter experts or reviewers to code documents, and the AI tool will learn from those decisions and predictive responsiveness, privilege, etc.The difference will be in the results. A more advanced AI tool’s predictions regarding privilege and responsiveness will be more accurate due to its ability to take nuance and context into consideration –leading to lower review costs and more accurate productions.ConclusionMany attorneys are still hesitant to move away from the older, AI eDiscovery tools they have used for the last decade. But today’s larger, more complicated datasets require more advanced AI tools. Attorneys who fear broadening their technology toolbox to include more advanced AI may find themselves struggling to stay within eDiscovery budgets, spending more time on finding and less time strategizing – and possibly even falling behind on their discovery obligations.But this fear and hesitancy can be overcome with education, transparency, and support from legal technology companies. Attorneys should look for the right technology partner who not only offers access to more advanced AI tools, but also provides implementation support and expert advisory services to help explain the technology and results to other stakeholders, opposing counsel, and judges.To learn more about the advantages of leveraging advanced AI within TAR workflows, download our white paper, “TAR + Advanced AI: The Future is Now.” And to discuss this topic more, feel free to connect with me at smoran@lighthouseglobal.com.ai-and-analytics; chat-and-collaboration-data; ediscovery-review; lighting-the-path-to-better-ediscoveryreview, ai-big-data, tar-predictive-coding, blog, ai-and-analytics, chat-and-collaboration-data, ediscovery-review,review; ai-big-data; tar-predictive-coding; blogai-analyticssarah moran

In the modern age of legal technology, cybersecurity and eDiscovery are unquestionably intertwined. As cybersecurity threats escalate and bad actors find success with new methods and sophisticated tools to gain access to the ever-growing volumes and types of confidential electronic data, legal departments and law firms are getting hit daily by cybersecurity incidents and breaches, with many not even knowing when the incidents have occurred. The legal world, and eDiscovery in particular, are enticing targets, as matters typically involve huge volumes of sensitive information and data often resides across multiple providers who play a part in the collection, processing, hosting, review, and production of data.From a security perspective, corporations are constantly dealing with the data their employees create, and thus they typically maintain a solid system focused on maintenance, protection, back-ups, and defense of that data. This internal process is implemented using governance, risk, and compliance standards that run pretty well from the inside. But security gaps arise when that data becomes subject to a legal hold for litigation and that once well-protected data gets sent out to law firms and/or outside providers.So how can organizations feel confident they’re effectively evaluating the cybersecurity stability of their law firms, third parties, cloud providers, etc.? Do your providers have relevant security controls in place to ensure your data resides in a reasonably similar method as you would store the data yourself? Here are the top three tips for structuring an effective and comprehensive eDiscovery security evaluation and creating a strong relationship with your providers:Leverage Industry-Standard CertificationsAt the security evaluation stage, it’s critical to get to know your providers well and develop trusted relationships. The best way to first evaluate their overall security is to leverage industry-standard certifications. If the provider has access to and holds your data, they should be able to demonstrate that they’re ISO 27001 and SOC 2 certified as those have become the standard security environment protocol in the eDiscovery industry. Industry-standard questionnaires such as the SIG can also be used to validate a provider’s security structure. If a provider already has a completed and updated the SIG, this can be immediately accepted without needing to recreate the wheel and require another type of basic security assessment. This should serve as your baseline and will aid your risk assessments overall. It’s also important for organizations to audit, on an annual basis, those fundamental controls your providers have in place as the industry continues to focus deeper into all areas of each certification. The days of checking the standard audits off your list and being considered compliant are quickly becoming a thing of the past. With the increase in breaches, we are also seeing deeper and more thorough inspections beyond your own company and a shift to the provider space. So make sure you’re getting involved and staying involved with your suppliers. They are critical elements of your success and you need to treat them as such.Devise Security Questions That Go Beyond the BasicsIn addition to the standard certifications and questions the SIG and other general security audits give you, it’s also important to go beyond the basics and devise questions for your eDiscovery vendors that will uncover any existing gaps. Outside of questionnaires that simply ask for “yes” or “no” answers, consider doing regular audits with specific and focused questions. For example, ask your providers to discuss what different technologies they’re considering in the next 12 months or what new security certifications they’re planning to pursue. This ensures that you’re acting in a forward-thinking manner and developing better insight into your partners’ future development. To combat the growing cybersecurity threat, organizations need to remain one step ahead and devise questions to find forward-thinking suppliers rather than ones that just check the boxes. It’s also crucial to apply focused energy to the evolution of the organization and its suppliers. Take the time to have open dialogue and explore different solutions with the goal of prevention of threats. In today’s market, most organizations are still operating in a reactive state, meaning solutions are in place to detect malicious behaviors already inside your boundaries. Remember the clock always wins and prevention is the preferred way to stay ahead of attacks. Ask your technology providers the tough questions around ransomware and look to see what kinds of SLAs or guarantees they can offer. This is a great place to start to separate products and services by the maturity of their offering.Consider a Managed Services EnvironmentIn the most ideal of situations, a corporation would know in advance their list of trusted providers for investigations and litigation, and they would have a regular flow of communication with those providers that includes updates on standard certifications as well as regular audits including questions that go beyond the basics. Many times, this secure workflow can be best served by establishing a dedicated managed services environment that can support a more seamless and secure flow of data when a matter transitions to eDiscovery. Taking advantage of the dedicated services that come with a managed services environment, the corporation gets a technically skilled and more diverse talent base to draw from – one that becomes an extension of your team and treats the security of your data as if it were their own. Within that environment, law firms and document review lawyers all log into the same database and a partnership develops between all parties, creating a more secure environment. In addition, you’ll see cost savings by not having to invest in your own security infrastructure and separate cybersecurity personnel.Overall, vendor security is an integral part of an organization’s cybersecurity strategy. It’s imperative for corporations who transfer sensitive data out of their control to third parties to make sure that each and every supplier who handles the data meets all of the organization’s internal security requirements, as well as established regulatory requirements. This can be achieved by choosing providers who maintain industry-standard security certifications, performing regular audits outside of standard security questionnaires, and at the most secure level, by creating a managed services environment with your suppliers. data-privacy; ediscovery-reviewcybersecurity, cloud-security, ediscovery-process, blog, data-privacy, ediscovery-reviewcybersecurity; cloud-security; ediscovery-process; bloglighthouse

Lawyers, and the legal field in general, are not exactly known for their willingness to embrace new technology and change the tried and true, traditional ways they’ve always used to practice law. But as technology has taken over our everyday lives and become the norm across most industries, there’s no time like the present for lawyers and litigation support professionals to take a second look at how they can get up to speed on the best and newest eDiscovery technology that will ultimately transform their business, and in turn, create happier clients who are laser focused on reducing costs and increasing efficiency.Like cassette tapes and the beloved Walkman, when it comes to eDiscovery, that old model of managing your own IT infrastructure and utilizing on-prem review platforms is becoming a thing of the past. This reminds me of other eDiscovery relics we knew and loved… not to call anyone out, but dare I mention Concordance or Clearwell in case you’re still using them?!In this changing technology landscape where most clients are moving (or have moved) their data to the cloud, it’s a perfect match to also modernize your law firm’s eDiscovery program and adopt a self-service, spectra model that will work seamlessly with data stored in the cloud, and deliver less risk and more benefits for both you and your clients.Just what are those benefits? Here are three reasons why adopting new technology and going to a SaaS eDiscovery solution will bring added efficiency, more billable hours, and happier clients.Eliminate the Risk and Expense of Managing Your Own IT Infrastructure - For law firms, managing an IT infrastructure and maintaining servers for the purpose of hosting client data is expensive and involves a large amount of risk. Electronic data has become overwhelmingly voluminous and types of data have become so much more complex than when law firms first got into this business and we were primarily dealing with email. Think about mobile devices, chat data, ephemeral communication, etc. as just the tip of the iceberg. With cybersecurity as a top concern for corporations, I think it’s fair to say that law firms probably never meant to take on the risk that comes with managing a complex IT infrastructure for their clients. Having a self-service, spectra, modern SaaS solution at their fingertips, law firms can lower costs and transfer the risk of hosting client data to the SaaS solution provider.Using SaaS Review Platforms Improves Client Services - Not only will a SaaS solution provide the benefit of relieving the security and risk burden, it will improve client services which is a win-win for the firm and the client. Although on-prem review platforms are what law firms have typically used, a SaaS platform reduces costs and improves efficiency. With an on-prem solution, license fees and infrastructure maintenance fees generally create out-of-pocket costs with no cost recovery mechanism. Moving to a SaaS solution introduces new ways to recover costs and makes solving substantive client concerns the primary job, rather than the inefficiencies that come along with maintaining an on-prem solution. To make the process of implementing a SaaS solution much easier, it’s important to note at this stage that building a business case and getting senior management on board with upgrading to a SaaS solution is critical. That way all parties understand the benefits to both the firm and its clients will be on the same page with making the change.Upgrading to SaaS Allows Firms to Provide the Latest Technology to Clients - Wouldn’t it be amazing if you could easily and quickly upgrade your eDiscovery technology and always provide the latest and greatest technology to clients? Moving to a SaaS platform immediately provides this benefit as the service provider maintains the infrastructure and makes technology upgrades behind the scenes for you. In case you’re feeling a little nervous that opportunity for some portions of internal work will disappear with this eDiscovery model, in fact the opposite is true. This isn’t a threat to the traditional litigation support model. It will instead allow for a greater focus on more valuable and strategic work while a solid partnership is established with the trusted service provider who runs the infrastructure of the SaaS platform and will work alongside you.If your primary goal is to create efficiency, lower costs, and ultimately make your clients happy, now’s the time to take your eDiscovery program to the next level and adopt a SaaS, self-service, spectra solution. You’ll have a modernized eDiscovery platform that allows for independent access and control to process, review, and produce data, while removing the risk and cost that comes with managing an IT infrastructure.ediscovery-review; ai-and-analyticscloud, self-service, spectra, cloud-security, blog, ediscovery-review, ai-and-analyticscloud; self-service, spectra; cloud-security; bloglighthouse

We often get questions from our clients about how best to select an electronic discovery vendor. Important considerations in this process are what questions to ask, how best to compare vendors and what are the important issues that are typically missed in the selection process. In particular, our clients often tell us that they sometimes struggle in the vendor selection phase to be able to best assess the quality and capabilities of a vendor. Given the challenges of choosing the right vendor, we often hear that law firms default to making their decision based almost exclusively on price considerations. Our list of questions can help you make the right decision based on more than just price.Top Questions To Ask When Choosing an eDiscovery VendorScope of ServicesWhat services does the vendor offer?If case parameters change, will the vendor be able to meet your needs and time frames?Are there volume benefits/discounts if you use multiple services (e.g. processing, hosting and production versus just hosting)?What services are sub-contracted out and does data ever leave the vendor’s site?What size or type of case is too big for the vendor?What have been vendor’s toughest cases?Expertise (Not all vendors are created equal; and it is not all about price)What is the vendor’s knowledge level of the technical issues?Are the vendor’s employees certified in the tools they use?What is the vendor’s level of understanding of the legal process?Are there legal professionals on staff?How does the vendor’s expertise compare to other vendors?Quality of ServicesIs this a vendor that you could see yourself establishing a longer term relationship?How does the vendor manage ensuring high quality service consistently: accurate and on-time?Are errors tracked? What are considered errors? How are errors addressed?What do the references say about the vendor?Customer ServiceWhat hours does the vendor operate?How available are the vendor’s employees during non-business hours?How much lead time is needed for processing and production?How are cases staffed?Who is the primary point of contact? Is it the same throughout the case?What is the nature of the vendor’s project management team and approach?How are issues escalated?Technical SpecificationsDoes the vendor use proprietary versus non-proprietary software and what are the benefits/trade-offs?If the data is not being processed locally, what is the vendor’s FTP connection speeds and how does this compare with the law firm’s FTP speeds?What is the vendor’s policy on backing up data?What is the vendor’s policy regarding storing data?ediscovery-reviewediscovery-process, blog, ediscovery-review,ediscovery-process; bloglighthouse

Minimizing Re-Review

By now, legal teams facing discovery are aware of many of the common technology and technology-enabled workflows used to increase the efficiency of document review on a single matter. But as data volumes grow and legal budgets shrink, legal teams must begin to think beyond a “matter-by-matter” approach. They must start applying technology more innovatively to create efficiencies across matters to minimize the burden of repeatedly reviewing the same documents again and again. Fortunately, many common technology-enabled review workflows (e.g., technology assisted review (TAR), advanced search guided by linguistic experts, and AI-powered review analytics) can help teams apply work product and insights from past matters to current and future matters. This not only saves time but also increases consistency and lowers the risk of inadvertent disclosures and cumbersome clawbacks.The opportunity to reduce repeated review is quite large, both because the problem is rampant and the technology that can help solve it is underutilized. A 2022 survey by the ABA showed that “predictive coding” is the least common application of eDiscovery software, used by only one in five law firms. In fact, 73% of respondents said they don’t know what predictive coding is (we explain it below). As document review continues to grow in complexity, and budget and other constraints apply pressure from other directions, more organizations should consider taking advantage of everything that technology has to offer.Repeated review is a large and familiar burdenRepeated review is baked into the status quo. Matters spanning multiple jurisdictions, civil litigations tied to government investigations, and matters involving the same or related IP are just a few examples in which the same documents could come up for review multiple times. Instead of looking across matters holistically, legal teams often feel obligated to roll up their sleeves, lower their heads, and review the same documents all over again—even when relevancy overlaps and for categories of information that remain relatively static across matters (privilege, trade secret, personally identifiable information (PII), etc.). This has obvious consequences for time and cost. The time invested on reviewing documents for privilege in a current matter, for example, becomes time saved on future matters involving those same documents. Risk is a factor as well. A document classified as privileged, or that contains PII or another sensitive category, in one matter should be classified the same way in the next one. But without a record of past matters, attorneys start over from scratch each time, which opens the door to inconsistency. And while it’s certainly possible to undo the mistake of producing sensitive documents, it can be quite time-consuming and expensive.Rejecting the status quo While the burden and risks associated with repeated review are felt every day, few legal teams and professionals are searching for a solution. Those willing to look beyond the status quo, however, will see that repeated review isn’t actually necessary, at least not to the degree that it’s done today. We also find that the keys to reducing repeated review lie in technology that many teams already use or have access to.Reusing work product from TAR and CAL workflows TAR 1.0, TAR 2.0, and Continuous Active Learning (CAL) workflows use machine learning technology to search and classify documents based on human input and their own ability to learn and recognize patterns. This is called predictive coding and it’s most often used to prioritize responsive documents for human review. The parameters for responsiveness change with the topics of each matter, so it’s not always possible to reuse those classifications on other matters. TAR and CAL tools can also be effective at making classifications around privilege, PII, and junk documents, which are not redefined from matter to matter. If a document was junk last time (say company logos attached to emails, blank attachments, etc.) it’s going to be junk this time too. Therefore, reusing these classifications made by technology on one matter can save legal teams even more time in the future. Refining review with linguistic expertsLinguistic experts add an extra layer of nuance to document review technology that makes them more precise and effective at classification. They develop complex criteria, based on intricate rules of syntax and language, to search and identify documents in a more targeted way than TAR and CAL tools.They can also help reduce repeated review by conducting bespoke searches informed by past matters. This process is more hands-on than using TAR and CAL tools; human linguists take lessons learned from one matter and incorporate them into their work on a related matter. It’s also more refined, so it can help in ways that TAR and CAL tools can’t.Litigation related to off-label drug use offers a good example. A company might have multiple matters tied to different drugs, making relevance unique for each matter. In this scenario, linguistics experts can identify linguistic markers that show how sales reps communicated with healthcare providers within that company. Then when the next off-label document review project begins, documents with those identifiers can be segregated for faster review. In this way, work from linguistic experts in one matter can help improve efficiency and minimize first-level review work on new matters. Apply learnings across matters using AI Review tools built on AI can reduce repeated review by classifying documents based on how they were classified before. AI tools can act as a “central mind” across matters, using past decisions on company data to make highly precise classifications on new matters. The more matters the AI is used on, the more precise its classifications become. The beauty here is that it applies to any amount of overlap across matters. The AI will recognize any documents that it has reviewed previously and will resurface their past classifications.Some AI tools can even retain the decision on past documents and associate it with a unique hash tag, so that it can tell reviewers how the same or similar documents were coded in previous matters—without the concern of over-retaining documents from past matters. Curious to challenge your status quo?TAR, AI, and other solutions can be invaluable parts of a legal team’s effort to curb repeated review — but they’re not the only part. In fact, the most important factor is a team’s mindset. It takes forethought and commitment to depart from the status quo, especially when it involves unfamiliar tools or strategies.The benefits can be profound, and the road to achieving them may be more accessible than you think.Find tips for starting small, as well as more information about how and why to address the burden of repeated review, in our deep dive on the subject.ai-and-analytics; ediscovery-review; lighting-the-path-to-better-ediscoveryreview, ai-big-data, blog, ai-and-analytics, ediscovery-reviewreview; ai-big-data; blogminimizing-re-reviewsarah moran

eDiscovery pricing has always been nuanced and inconsistent across vendors and technology providers, making it difficult for law firms and corporations alike to compare and contrast options. So, it is no big surprise that this same challenge exists across self-service, spectra eDiscovery tools and software as well, making it extremely challenging to model out an apples-to-apples comparison across solutions. This inability to accurately compare costs across different platforms leaves you and your team in the dark when it comes to choosing the right tool and pricing model to fit your needs.The ChallengeToday’s self-service, spectra solutions are frequently priced based on data sizes/volumes at different phases of the eDiscovery processing, review, and production workflow, often with each of these steps having their own cost trigger associated with them. For example, many solutions charge based off of hosted volume, raw data size, or even post-extraction data volume, while others charge a flat-fee per matter.Although attractive on their face, on a per matter plan you may be in good shape if you are able to entirely self-support, but any requests for help or training are frequently not included in the flat-fee. The lack of the ability to predict the future needs for support make the flat-fee a riskier choice. While paying for eDiscovery based off of per GB sizes is a very popular method, not knowing the expansion rates, the hosted volume, or not needing an entire data-set post culling and filtering means you may fall victim to data size anomalies or paying for data you don’t need.Lastly it is important that you make sure to understand the full ecosystem of potential charges to avoid any surprises. Make sure to understand if there are other costs or “gotchas” you need to be aware of around user fees, OCR costs, Bates endorsement fees, user trainings, additional license fees to other platforms, costs to process specific file types, language translation, etc. Not to mention, you also have to consider the various technology platforms that are included and assess the need for ongoing expert support.There is no perfect pricing model. The key to all of this is choosing the right model for you and your eDiscovery profile, but, how do you go about that?The SolutionWhen you are evaluating pricing for self-service, spectra platforms and you have narrowed down to a few technology providers that offer technology that fits your needs, make sure to leverage the following tips to ensure you’re making an informed comparison:Trust but verify. Ask the technology provider to explain the price point and both how and when it is measured. Discuss how the confluence of the eDiscovery workflow and cost actually come together. Will all workflows trigger all cost points? Do you have to pay for technology you don’t want or need? Understanding the answers to these questions will allow you to get a big picture understanding and to get a feel for where you may see your costs rise or decrease.Set up a real case example. Ask the potential providers to use your actual data volumes to illustrate what the cost would like look for a given period of time (i.e. a month or even a few years). This will allow you to see what actuals would be across platforms as well as give you the opportunity to explore different pricing models with the vendor to meet your budgetary constraints.Use a pricing calculator. Create a pricing calculator to compare self-service, spectra tools. Add as many variables as you would like to understand across all possible scenarios and across the different platforms you are evaluating. Leverage this to compare bottom-line numbers and determine the right fit for you. Additionally, lean on your vendor to help you build this out and make a comparison.To discuss this topic more or to learn how we can help you make an apples-to-apples comparison, feel free to reach out to me at bthompson@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, ediscovery-process, blog, ediscovery-review,cloud; self-service, spectra; ediscovery-process; blogbrooks thompson

On July 15, 2022, the mandatory Disclosure Pilot Scheme (PD51U) was officially approved and will operate on a permanent basis within the Business and Property Courts (BP&C) of England and Wales. Originally implemented in 2019 on a temporary pilot basis, it was extended twice and had been set to expire in December of 2022. Its approval means that on October 1, 2022, the pilot will end, and the scheme will officially be known as Practice Direction (PD) 57AD “Disclosure in the Business and Property Courts.”This approval is no surprise to those familiar with the modern disclosure process in the UK. PD51U was originally implemented to address the key issues associated with standard disclosure under Civil Procedure Rule (CPR) 31, such as unwieldly costs and the insurmountable scale of disclosure due to ever-growing corporate data volumes. As per UTB LLC v Sheffield United, the pilot was meant to effect a “culture change” in the reasonableness and proportionality of disclosure requests by streamlining the process in a variety of ways. One of the most notable is through the encouragement of leveraging technology (such as technology assisted review or TAR) and data analytics for document review—even going so far as to mandate the use of TAR in cases where the document count exceeds 50,000.Over the last two years, this push toward implementing more technology to streamline the disclosure process has proven to be a wise one. With a worldwide shift to cloud-based infrastructures and remote working, corporate data volumes have exploded and will only continue to grow. Therefore, the traditional means of disclosure review, wherein a team of reviewers looks at each electronic document one-by-one, is quickly becoming untenable. Utilising technology to streamline review is more imperative than ever and will only grow in importance as data volumes continue to balloon. What 57AD does not mean, however, is that solicitors faced with disclosure need to be data science or technology experts. It simply means that it will become increasingly important for solicitors who are not comfortable with disclosure technology to find a solid managed review partner that can help streamline the disclosure process with technology and meet Practice Direction 57AD requirements. Below are key attributes to look for when seeking such a partner.Look for a managed review partner with expertise on the Disclosure Review Document (DRD)The DRD is meant to facilitate an agreement between parties about what constitutes proportional disclosure, and how to achieve that goal in a cost-effective manner. To do so, it requires parties to identify the key issues of the case and then detail the method of disclosure for each issue, with five methods from which to choose.[1] Each method can have severe impacts on the cost of a matter, as well as the overall outcome of the case for clients. It is vital that someone with in-depth disclosure expertise is involved in the negotiation and completion of this document. Some managed review vendors may be able to provide staffing and project management when it comes to disclosure document review but will not have experts available and capable to provide advice on effective disclosure strategy, including DRD assistance. Without this expertise, a party may find itself agreeing to disclosure methods that significantly balloon budgets or even worse, result in harmful outcomes for clients. Look for a managed review partner who has developed strong defensible workflowsOne of the hallmarks of and impetuses for PD 51U (soon to be PD 57AD) was to streamline the disclosure process in the face of ever-growing and unprecedented data volumes. Understanding when and how to leverage technology to cull and prioritise data for review, as well as how to leverage TAR, is imperative. However, the technology and workflows can seem overwhelming, especially to those who don’t perform disclosure often. Thus, it is essential to find a managed review partner who has access to the best review technology and knows how to leverage that technology to achieve the best results in every type of matter. It is also important that that managed review partner has developed strong defensible workflows for data reduction that can be customised to meet the individual needs of each client.Look for a managed review partner who thinks outside of the traditional linear review approachWhile it may seem simpler to fall back on traditional approaches to the disclosure document review process (i.e., hiring many reviewers to read and categorize each document), it is important to remember that PD 57AD was enacted because that approach is quickly becoming too burdensome for parties. The traditional approach also opens parties up to risk, when reviewers cannot effectively review the volume of documents within the time frames required for disclosure. Today’s larger data volumes and more complicated data increase the risk that human reviewers will miss important documents that were required to be disclosed, or conversely, that they will disclose harmful or sensitive documents that should not have been disclosed. Forward-thinking managed review partners have anticipated this change and have invested in technology and human expertise that can defensibly minimise document volumes so that a discrete number of subject matter experts can look at prioritised categories of pertinent documents, maximizing the value of human review. In this way, a managed reviewer partner can help solicitors move away from an outdated approach to review, while streamlining the disclosure process, keeping litigation budgets in check, minimising risk, and achieving better outcomes. Look for a partner who will help prepare bespoke briefing documentation, right from the outsetWhen a matter needs to scale up quickly and on short notice, the painstaking process of adding new reviewers can explode budgets—not only because of the additional overhead, but also because of the churn and inefficiency created by inconsistent work product from inexperienced, new reviewers. A good managed review partner will prepare for and minimise this churn from the outset, by creating customised briefing documentation that enables new reviewers to roll onto matters seamlessly, without a heavy lift from the client or review manager. Documentation like term glossaries for niche cases (for example, medical inquiries) that are kept in a central repository will help case teams quickly scale up and onboard new reviewers at short notice, while minimizing the churn and risk often thought of as inevitable when adding new reviewers. Look for a partner who has developed ways to ensure quality work from review teamsInconsistent or incorrect decisions from review teams creates additional work, which can decimate budgets. Even when data volumes are culled to more manageable levels, inaccurate review work product can still open clients up to risk, especially when sensitive data is involved. Look for managed review partners who have systems in place to ensure the accuracy of the review team from the outset. For example, some managed review providers will rigorously “test” the work product of review teams, directly after training has finished. This testing process can ensure that each reviewer assigned to the team understands the subject matter and review process, and that from the start of the matter their work product aligns with the case team’s direction. This type of quality control, started at the reviewer selection process, can greatly reduce risk while keeping budgets under control. Look for a managed review partner who ensures value for money in terms of candidatesIn a traditional approach, first pass review for relevance, privilege, and issues are undertaken by UK-based paralegals, with proven experience in reviewing and redacting documents together with a law degree, LPC/GDL, or NALP certification. However, these reviewers can be expensive, and billed at exorbitant hourly rates. Forward-thinking managed review partners often have partnerships with reviewers who have been admitted to Bars outside of the UK, providing an added layer of experience offered at a reduced cost. This complies with the overall message of PD 57AD, in that it offers a reliable basis for costs which promotes the cost-effective and efficient conduct of disclosure. [1] Model A – No order for disclosure; Model B – Limited disclosure; Model C – Request-led, search-based disclosure; Model D – Narrow search-based disclosure (with or without narrative documents); Model E – Wide search-based disclosureediscovery-reviewreview, blog, ediscovery-review,review; blogjennifer cowman

A recent conversation with a colleague in Lighthouse’s Focus Discovery team resonated with me – we got to chatting about TAR protocols and the evolution of TAR, analytics, and AI. It was only five years ago that people were skeptical of TAR technology and all the discussions revolved around understanding TAR and AI technology. That has shifted to needing to understand how to evaluate the process of your team or of opposing counsel’s production. Although an understanding of TAR technology can help in said task, it does not give you enough to evaluate items like the parity of types of sample documents, the impact of using production data versus one’s own data, and the type of seed documents. That discussion prompted me to grab one of our experts, Tobin Dietrich, to discuss the cliff notes of how one should evaluate a TAR protocol. It is not totally uncommon for lawyers to receive a technology assisted review methodology from producing counsel – especially in government matters but also in civil matters. In the vein of the typical law school course, this blog will teach you how to issue spot if one of those methodologies comes across your desk. Once you’ve spotted the issues, bringing in the experts is the right next step.Issue 1: Clear explanation of technology and process. If the party cannot name the TAR tool or algorithm they used, that is a sign there is an issue. Similarly, if they cannot clearly describe their analytics or AI process, this is a sign they do not understand what they did. Given that the technology was trained by this process, this lack of understanding is an indicator that the output may be flawed.Issue 2: Document selection – how and why. In the early days of TAR, training documents were selected fairly randomly. We have evolved to a place now where people are being choosy about what documents they use for training. This is generally a positive thing but does require you to think about what may be over or under represented in the opposing party’s choice of documents. More specifically, this comes up in 3 ways:Number of documents used for training. A TAR system needs to understand what responsive and non-responsive looks like so it needs to see many examples in each category to approach certainty on its categorization. When using too small a sample, e.g. 100 or 200 documents, this risks causing the TAR system to incorrectly categorize. Although a system can technically build a predictive model from a single document, it will only effectively locate documents that are very similar to the starting document. The reality of a typical document corpus is that it is not so uniform as to rely upon the single document predictive model.Types of seed documents. It is important to use a variety of documents in the training. The goal is to have the inputs represent the conceptual variety in the broader document corpus. Using another party’s production documents, for example, can be very misleading for the system as the vocabulary used by other parties is different, the people are different, and the concepts discussed are very different. This can then lead to incorrect categorization of documents. Production data, specifically, can also add confusion with the presence of Bates or confidentiality stamps. If the types of seed documents/training documents used do not mirror typical types of documents expected from the document corpus, you should be suspicious.Parity of seed document samples. Although you do not need anything approaching the perfect parity of responsive and non-responsive documents, it can be challenging to use 10x the number of non-responsive versus responsive documents. This kind of disparity can distort the TAR model. It can also exacerbate either of the above issues, number, or type of seed documents.Issue 3: How is performance measured? People throw around common TAR metrics like recall and precision without clarifying what they are referring to. You should always be able to tell what population of documents these statistics relate to. Also, don’t skip over precision. People often throw out recall as sufficient, but precision can provide important insight into the quality of model training as well.By starting with these three areas, you should be able to flag some of the more common issues in TAR processes and either avoid them or ask for them to be remedied. ai-and-analytics; ediscovery-reviewanalytics, ai-big-data, tar-predictive-coding, blog, ai-and-analytics, ediscovery-reviewanalytics; ai-big-data; tar-predictive-coding; bloglighthouse

Cut-off scores, seed sets, training rounds, confidence levels – to the inexperienced, technology assisted review (TAR) can sound like a foreign language and can seem just as daunting. Even for those legal professionals who have had experience utilizing the traditional TAR 1.0 model, the process may seem too rigid to be useful for anything other than dealing with large data volumes with pressing deadlines (such as HSR Second Requests). However, TAR 2.0 models are not limited by the inflexible workflow imposed by the traditional model and require less upfront time investment to realize substantial benefits. In fact, TAR 2.0 workflows can be extremely flexible and helpful for myriad smaller matters and non-traditional projects, including everything from an initial case assessment and key document review to internal investigations and compliance reviews.A Brief History of TARTo understand the various ways that TAR 2.0 can be leveraged, it will be helpful to understand the evolution of the TAR model, including typical objections and drawbacks. Frequently referred to as predictive coding, TAR 1.0 was the first iteration of these processes. It follows a more structured workflow and is what many people think of when they think of TAR. First, a small team of subject-matter experts must train the system by reviewing control and training sets, wherein they tag documents based on their experience with and knowledge of the matter. The control set provides an initial overall estimated richness metric and establishes the baseline against which the iterative training rounds are measured. Through the training rounds, the machine develops the classification model. Once the model reaches stability, scores are applied to all the documents based on the likelihood of being relevant, with higher scores indicating a higher likelihood of relevance. Using statistical measures, a cutoff point or score is determined and validated, above which the desired measure of relevant documents will be included. The remaining documents below that score are deemed not relevant and will not require any additional review.Although the TAR 1.0 process can ultimately result in a large reduction in the number of documents requiring review, some elements of the workflow can be substantial drawbacks for certain projects. The classification model is most effectively developed from accurate and consistent coding decisions throughout the training rounds, so the team of subject-matter experts conducting the review are typically experienced attorneys who know the case well. These attorneys will likely have to review and code at least a few thousand documents, which can be expensive and time consuming. This training must also be completed before other portions of the document review, such as privilege or issue coding, can begin. Furthermore, if more documents are added to the review set after the model reaches stability (think, a refresh collection or late identified custodian) the team will need to resume the training rounds to bring the model back to stability for these newly introduced documents. For these reasons, the traditional TAR 1.0 model is somewhat inflexible and suited best for matters where the data is available upfront and not expected to change over time (i.e. no rolling collections) so that the large number of documents being excised from the more costly document review portion of the project will offset the upfront effort expended training the model.TAR 2.0, also referred to as continuous active learning (CAL), is a newer workflow (although it has been around for a number of years now) that provides more flexibility in its processes. Using CAL, the machine also learns as the documents are being reviewed, however, the initial classification model can be built with just a handful of coded documents. This means the review can begin as soon as any data is loaded into the database, and can be done by a traditional document review team right from the outset (i.e. there is no highly specialized “training” period). As the documents are reviewed, the classification model is continuously updated as are the scores assigned to each document. Documents can be added to the dataset on a rolling basis without having to restart any portion of the project. The new documents are simply incorporated into the developing model. These differences make TAR 2.0 well suited for a wider variety of cases and workflows than the traditional TAR 1.0 model.TAR 2.0 Workflow ExamplesOne of the most common TAR 2.0 workflows is a “prioritization review,” wherein the highest scoring documents are pushed to the front of the review. As the documents are reviewed the model is updated and the documents are rescored. This continuous loop allows for the most up-to-date model to identify what documents should be reviewed next, making for an efficient review process, with several benefits. The team will review the most likely relevant, and perhaps important, documents first. This can be especially helpful when there are short timeframes within which to begin producing documents. While all documents can certainly be reviewed, this workflow also provides the means to establish a cutoff point (similar to TAR 1.0) where no further review is necessary. In many cases, when the review reaches a point where few relevant documents are found, especially in comparison to the number of documents being reviewed, this point of diminishing returns signals the opportunity to cease further review. The prioritization review can also be very effective with incoming productions, allowing the system to identify the most relevant or useful documents.An alternative TAR 2.0 workflow is the “coverage” or “diverse” review model. In this model, rather than reviewing the highest scoring documents first, the review team focuses on the middle-scoring range documents. The point of a diverse review model is to focus on what the machine doesn’t know yet. Reviewing the middle range of documents further trains the system. In this way, a coverage TAR 2.0 review model provides the team with a wide variety of documents within the dataset. When using this workflow for reviews for productions, the goal is to end up with the documents separated between those likely relevant and those likely not relevant. This workflow is similar to the TAR 1.0 workflow as the desired outcome is to identify the relevant document set as quickly or directly as possible without reviewing all of the documents. To illustrate, a model will typically begin with a bell-shaped curve of the distribution of documents across the scoring spectrum. This workflow seeks to end with two distinct sets, where one is the relevant set and the other is the non-relevant set.These workflows can be extremely useful for initial case assessments, compliance reviews, and internal investigations, where the end goal of the review is not to quickly find and produce every relevant document. Rather, the review in these types of cases is focused on gathering as much relevant information as possible or finding a story within the dataset. Thus, these types of reviews are generally more fluid and can change significantly as the review team finds more information within the data. New information found by the review team may lead to more data collections or a change in custodians, which can significantly change the dataset over time (something TAR 2.0 can handle but TAR 1.0 cannot). And because the machine provides updated scoring as the team investigates and codes more documents, it can even provide the team with new investigational avenues and leads. A TAR 2.0 workflow works well because it gives the review team the freedom to investigate and gain knowledge about a wide variety of issues within the documents, while still ultimately resulting in data reduction.ConclusionThe above workflow examples illustrate that TAR does not have to be the rigid, complicated, and daunting workflow feared by many. Rather, TAR can be a highly adaptable and simple way to gain efficiency, improve end results, and certainly to reduce the volume of documents reviewed across a variety of use cases.It is my hope that I have at least piqued your interest in the TAR 2.0 workflow enough that you’ll think about how it might be beneficial to you when the next document review project lands on your desk.If you’re interested in discussing the topic further, please freely reach out to me at DBruno@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewtar-predictive-coding, blog, ai-and-analytics, ediscovery-reviewtar-predictive-coding; blogdavid bruno

Given the proliferation of data and evolving variety of data sources, in-house counsel teams are beginning to exhaust resources managing increasingly complex case data. self-service, spectra eDiscovery legal technology offers a compelling solution. Consider the impact of inefficiencies faced by in-house counsel, today - from waiting for vendors to load data or provide platform access, to scrambling, to keeping up with advancing technologies, and managing data security risks - it’s a lot. The average in-house counsel team isn’t just dealing with these inefficiencies on large litigations, they’re encountering these issues in even the smallest compliance and internal investigations matters.self-service, spectra solutions offer an opportunity to streamline eDiscovery programs, allowing in-house legal teams to get back to the business of case management and legal counseling. It’s understandable we’re witnessing more and more companies moving to this model.So, once your organization has decided it is ready to step into the future and take advantage of the benefits self-service, spectra eDiscovery solutions have to offer, what’s next? Below, I’ve outlined three best practices for implementing a self-service, spectra eDiscovery solution within your organization. While any organizational change can seem daunting at the outset, keeping the below tips in mind will help your company seamlessly move to a self-service, spectra model.1. Define how you leverage your self-service, spectra eDiscovery solution to scale with ease.One of the key benefits of a quality self-service, spectra solution is that it puts your organization back in the eDiscovery driver’s seat. You decide what cases you will handle internally, with the advantage of having access to an array of eDiscovery expertise and matter management services when needed, even if that need arises in the middle of an ongoing matter. Cloud-based self-service, spectra solutions can readily handle any amount of data, and a quality self-service, spectra solution provider will be able to seamlessly scale up from self-service, spectra to full-service without any interruption to case teams.Having a plan in place regarding how and when you will leverage each of these benefits (i.e. self-service, spectra vs. full-service) will help you manage internal resources and implement a pricing model that fits your organization’s needs.2. Select a pricing model that works for your organization.Every organization’s eDiscovery business is different and self-service, spectra pricing models should reflect that. After determining how your organization will ideally leverage a self-service, spectra platform, decide what pricing model works best for that type of utilization. self-service, spectra solution providers should be able to provide a variety of licensing options to choose from, from an a la cart approach to subscription and transaction models.Prior to communicating with your potential solution provider, define how you plan to leverage a self-service, spectra solution to meet your needs. Then you can consider the type of support you require to balance your caseload with team resources and prepare to talk to providers about whether they can accommodate that pricing. Once you have on-boarded a self-service, spectra solution, be sure to continue to evaluate your pricing model, as the way you use the solution may change over time.3. Discuss moving to a self-service, spectra model with your IT and data security teams .Another benefit of moving to a self-service, spectra model is eliminating the burden of application and infrastructure management. Your in-house teams will be able to move from maintaining (and paying for) a myriad of eDiscovery technologies to a single platform providing all of the capabilities you need without the IT overhead. In effect, moving to a self-service, spectra solution gives your team access to industry-leading eDiscovery technology while removing the cost and hassle of licensing and infrastructure upkeep.A self-service, spectra model also allows you to transfer some of your organization’s data security risk to a solution provider. You gain peace of mind knowing your eDiscovery data and the supporting tech is administered by a dedicated IT and security team in a state-of-the-art IT environment with best-in-class security certifications.Finally, to ensure your organization can realize the full benefit of moving to a self-service, spectra solution, it’s imperative that your IT team has a seat at the table when selecting a solution platform. They can help to ensure that whatever service is selected can be fully and seamlessly integrated into your organization’s systems. Keeping these tips in mind as your organization begins its self-service, spectra journey will help you realize the benefits that a quality self-service, spectra eDiscovery platform can provide. For more in-depth guidance on migrating to self-service, spectra platforms, Brooks Thompson’s blog posts discussing tips for overcoming self-service, spectra objections and building a self-service, spectra business case.ediscovery-review; ai-and-analyticsself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; bloglighthouse

self-service, spectra as a topic has grown significantly in the recent past. With data proliferating at astronomical amounts year over year it makes sense that corporations and firms are wanting increasing control over this process and its cost. Utilizing a self-service, spectra eDiscovery tool is helpful if you want control over your queue as well as your hosted footprint. It is beneficial if your team has an interest and the capability of doing your own ECA. Additionally, self-service, spectra options are useful as they provide insight into specific reporting that you may or may not be currently receiving.Initially, the self-service, spectra model was introduced to serve part of the market that didn’t require such robust, traditional full eDiscovery services for every matter. Tech-savvy corporations and firms with smaller matters were delighted to have the option to do the work themselves. Over time there have been multiple instances in which a small matter scales unexpectedly and must be dealt with quickly, in an all hands on deck approach, to meet the necessary deadlines. In these instances, it’s beneficial to have the ability to utilize a full-service team. When these situations arise it’s critical to have clean handoffs and ensure a database will transfer well.Moreover, we have seen major strides in the self-service, spectra space regarding the capabilities of data size thresholds. self-service, spectra options can now handle multiple terabytes, so it’s not just a “small matter” solution anymore. This gives internal teams incredible leverage and accessibility not previously experienced.self-service, spectra considerations and recommendationsIt’s important to understand the instances in which a company should utilize a self-service, spectra model or solution. Thus, I recommend laying out a protocol. Put a process in place ahead of time so that the next small internal investigation that gets too large too quickly has an action plan that gets to the best solution fast. Before doing this, it’s important to understand your team’s capabilities. How many people are on your team? What are their roles? Where are their strengths? What is their collective bandwidth? Are you staffed for 24/7 support or second requests or are you not?Next, it’s time to evaluate what part of the process is most beneficial to outsource. Who do you call for any eDiscovery related need? Do you have a current service provider? If so, are they doing a good job? Are they giving you a one-size-fits-all solution (small or large), or are they meeting you where you are and acting as a true partner? Are they going the extra mile to customize that process for you? It’s important to continually audit service providers.Think back to past examples. How prepared has your team and/or service provider been in various scenarios? For instance, if an investigation is turning into a government investigation, do you want your team pushing the buttons and becoming an expert witness, or do you have a neutral third party to hand that responsibility off to?After the evaluation portion, it’s time to memorialize the process through a playbook, so that everyone has clear guidelines regardless of which litigator or paralegal internally is working on the case. What could sometimes be a complicated situation can be broken down into simple rules. If you have a current protocol or playbook, ensure your team understands it. Outline various circumstances when the team would utilize self service or full service, so everyone is on the same page.For more on this topic, check out the interview on the Law & Candor podcast on scaling your eDiscovery program from self service to full service. ediscovery-reviewcloud, self-service, spectra, cloud-services, blog, ediscovery-review,cloud; self-service, spectra; cloud-services; bloglighthouse

In this two-part series, I interview Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP about his thoughts on the state of eDiscovery within law firms today, including lessons learned and best practices to help attorneys overcome their trepidation of electronic discovery and build a better litigation practice. This first blog focuses on the history of eDiscovery and the logical reasons that attorneys may still try to avoid it, often to the detriment of their clients and their overall practice. IntroductionThe term “eDiscovery” (i.e., electronic discovery) was coined circa 2000 and received significant consideration by The Sedona Conference and others, well in advance of November 2006. That’s when the U.S. Supreme Court amended the Federal Rules of Civil Procedure to include electronically stored information (ESI), which was widely recognized as categorically different from data printed on paper. The amendments specifically mandated that electronic communications (like email and chat) would have been preserved in anticipation of litigation and produced when relevant. In doing so, it codified concepts explored by Judge Shira Scheindlin’s groundbreaking Zubulake v. UBS Warburg decisions.By 2012, the exploding volumes of data led technologists assisting attorneys to employ various forms of artificial intelligence (AI) to allow analysis of data to be accomplished in blocks of time that were still affordable to litigants. The use of predictive coding and other forms of technology-assisted review (TAR) of ESI became recognized in U.S. courts. By 2013 updates to the American Bar Association (ABA) Model Rules of Professional Conduct officially required attorneys to stay current on “the benefits and risks” of developing technologies. By 2015, the FRCP was amended again to help limit eDiscovery scope to what is relevant to the claims and defenses asserted by the parties and “proportional to the needs of the case,” as well as to normalize judicial treatments of spoliation and related sanctions associated with ESI evidence. In the same year, California issued a formal ethics opinion obligating attorneys practicing in California to stay current with ever changing eDiscovery technologies and workflows in order to comply with their ethical obligation of competently providing legal services.In the 15 years that have passed since those first FRCP amendments designed to deal with the unique characteristics of ESI, we’ve seen revolutionary changes in the way people communicate electronically within organizations, as well as explosive growth in the volume and variety of data types as we have entered the era of Big Data. From the rise of email, social media, and chat as dominant forms of interpersonal communication, to organizations moving their data to the Cloud, to an explosion of ever-changing new data sources (smart devices, iPhones, collaboration tools, etc.) – the volume and variety of which makes understanding eDiscovery’s role in litigation more important than ever.And yet, despite more than 20 years of exposure, the challenges of eDiscovery (including managing new data forms, understanding eDiscovery technology, and adhering to federal and state eDiscovery standards) continue to generate angst for most practitioners.So why, in 2021, are smart, sophisticated lawyers still uncomfortable addressing eDiscovery demands and responding to them? To find out, I went to one of the leading experts in eDiscovery today, Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP. Mr. Calhoun has over 40 years of experience in litigation and counseling, and he currently serves as Chair of the firm’s Electronic Discovery, Information Management & Compliance Practice. Over the years he has found creative solutions to eDiscovery challenges, like having a court enter a case management order requiring all 42 parties in a complex construction defect case to use a single technology provider, which dropped the technology costs to less than 2.5% of what they would have been had each party employed its own vendor. In another case (which did not involve privileged communications), he was able to use predictive coding to rank 600,000 documents and place them into tranches from which samples were drawn to determine which tranches could be produced without further review. It was ultimately determined that about 35,000 documents would not have to be reviewed after having put eyes on fewer than 10,000 of the original 600,000.I sat down with Mr. Calhoun to discuss his practice, his views of the legal and eDiscovery industries, and to try to get to the bottom of how attorneys can master the challenges posed by eDiscovery without having to devote the time needed to become an expert in the field.Let’s get right down to it. With all the helpful eDiscovery technology that has evolved in the market over the last 10 years, why do you think eDiscovery still poses such a challenge for attorneys today? Well, right off the bat, I think you’re missing the mark a bit by focusing your inquiry solely around eDiscovery technology. The issue for many attorneys facing an eDiscovery challenge today is not “what is the best eDiscovery technology?” – because many attorneys don’t believe any eDiscovery technology is the best “solution.” Many believe it is the problem. No technology, regardless of its efficacy, can provides value if it is not used. The issue is more fundamental. It’s not about the technology, it is about the fear of the technology, the fear of not being able to use it as effectively as competitors, and the fear of incurring unnecessary costs while blowing budgets and alienating clients.Practitioners fear eDiscovery will become a time and money drain, and attorneys fear that those issues can ultimately cost them clients. Technology may, in fact, be able to solve many of their problems – but most attorneys are not living and breathing eDiscovery on a day-to-day basis (and, frankly, don’t want to). For a variety of reasons, most attorneys don’t or can’t make time to research and learn about new technologies even when they’re faced with a discovery challenge. Even attorneys who do have the inclination and aptitude to deal with the mathematics and statistical requirements of a well-planned workflow, who understand how databases work, and who are unfazed by algorithms and other forms of AI, often don’t make the time to evaluate new technology because their plates are already full providing other services needed by their clients. And most attorneys became lawyers because they had little interest in mathematics, statistics, and other sciences, so they don’t believe they have the aptitude necessary to deal with eDiscovery (which isn’t really true). This means that when they’re facing gigabytes or even terabytes of data that have to be analyzed in a matter of weeks, they often panic. Many lawyers look for a way to make the problem go away. Sometimes they agree with opposing counsel not to exchange electronic data; other times they try to bury the problem with a settlement. Neither approach serves the client, who is entitled to an expeditious, cost effective, and just resolution of the litigation. Can you talk more about the service clients are entitled to, from an eDiscovery perspective? By that, I mean – can you explain the legal rules, regulations, and obligations that are implicated by eDiscovery, and how those may impact an attorney facing an electronic discovery request? Sure. Under Rule 1 of the FRCP and the laws of most, if not all, states, clients are entitled to a just resolution of the litigation. And ignoring most of the electronic evidence about a dispute because a lawyer finds dealing with it to be problematic rarely affords a client a just result. In many cases, the price the client pays for counsel’s ignorance is a surcharge to terminate the litigation. And, counsel’s desire to avoid the challenge of eDiscovery very often amounts to a breach of the ethical duty to provide competent legal services.The ABA Model Rules (as well as the ethical rules and opinions in the majority of states) also address the issue. The Model Rules offer a practitioner three alternatives when undertaking to represent a client in a case that involves ESI (which almost every case does). To meet his or her ethical obligation to provide competent legal services, the practitioner can: (1) become an expert in eDiscovery matters; (2) team up with an attorney or consultant who has the expertise; or (3) decline the engagement. Because comparatively few attorneys have the aptitude to become eDiscovery experts and no one who wants to practice law can do so by turning down virtually all potential engagements, the only practical solution for most practitioners is finding an eDiscovery buddy.In the end, I think attorneys are just looking for ways to make their lives (and thereby their clients’ lives) easier and they see eDiscovery as threatening to make their lives much harder. Fortunately, that doesn’t have to be the case.So, it sounds like you’re saying that despite the fact that it may cost them clients, there are sophisticated attorneys out there that are still eschewing legal technology and responding to discovery requests the way they did when most discovery requests involved paper documents? Absolutely there are. And I can empathize with their thought process, which is usually something along the lines of “I don’t understand eDiscovery technology and I’m facing a tight discovery deadline. I do know how to create PDFs from scanned copies of paper documents and redact them, if necessary. I’m just going to use the method I know and trust.” While this is an understandable way to think, it will immediately impose on clients the cost of inefficient litigation and settlements or judgments that could have been reduced or avoided if only the evidence had been gathered. Ultimately, when the clients recognize that their counsel’s fear of eDiscovery is imposing a cost on them, that attorney will lose the client. In other words, counsel who refuse to delve into ESI because it is hard is similar to a person who lost car keys in a dark alley but insists on only looking under the streetlight because it is easier and safer than looking in the dark alley.That’s such a great analogy. Do you have any real-world examples that may help folks understand the plight of an attorney who is basically trying to ignore ESI?Sure. Here’s a great example: Years ago, my good friend and partner told me he would retire without ever having to learn about eDiscovery. My partner is a very successful attorney with a great aptitude for putting clients at ease. But about a week after expressing that thought, he came to me with 13 five-inch three-ring binders. He wanted help finding contract paralegals or attorneys to prepare a privilege log listing all the documents in the binders. An arbitrator had ordered that if he did not have a privilege log done in a week, his expert would not be able to testify. His “solution” was to rent or buy a bunch of dictating machines and have the reviewers dictate the information about the documents and pay word processers overtime to transcribe the dictation into a privilege log. I asked what was in the binders. Every document was an email thread and many had families. My partner had received the data as a load file, but he had the duplications department print the contents rather than put them into a review platform. Fortunately, the CD on which the data was delivered was still in the file.I can tell this story now because he has since turned into quite the eDiscovery evangelist, but that is exactly the type of situation I’m referring to: smart, sophisticated attorneys who are just trying to meet a deadline and stay within budget will do whatever takes to get the documents or other deliverable (e.g., a privilege log) out the door. And without the proper training, unfortunately, the solution is to throw more bodies at the problem – which invariably ends up being more costly than using technology properly.Can you dive a bit deeper there? Explain how performing discovery the old-fashioned way on a small case like that would cost more money than performing it via a dedicated eDiscovery technology.Well, let me finish my story and then we’ll compare the cost of using 20th and 21st Century technologies to accomplish the same task. As I said, when I agreed to help my partner meet his deadline, I discovered all the notebooks were filled with printed copies of email threads and attachments. My partner received a load file with fewer than 2 GBs and gave it to the duplications department with instructions to print the data so he could read it. We gave the disk to an eDiscovery provider, and they created a spreadsheet using the email header metadata to populate the log information about who the record was from, who it was to, who was copied, whether in the clear or blind, when it was created, what subject was addressed, etc. A column was added for the privilege(s) associated with the documents. Those before a certain date were attorney-client only. Those after litigation became foreseeable were attorney-client and work product. That made populating the privilege column a snap once the documents were chronologically arranged. The cost to generate the spreadsheet was a few hundred dollars. Three in-house paralegals were able to QC, proofread, and finalize the log in less than three days for a cost of about $2,000.Had we done it the old-fashioned way, my partner was looking at having 25 or 30 people dictating for five days. If the reviewers were all outsourced, the cost would have been $12,000 to $15,000. He planned to use a mix of in-house and contract personnel - so, the cost would have been 30% to 50% higher. The transcription process would have added another $10,000. The cost of copying the resulting privilege log that would have been about 500 pages long with 10 entries per page for the four parties and arbitrator would have been about $300. So even 10 years ago, the cost of doing things the old-fashioned way would have been about $35,000. The technology-assisted solution was about $2,500. Stay tuned for the second blog in this series, where we delve deeper into how attorneys can save their clients money, achieve better outcomes, and gain more repeat business once they overcome common misconceptions around eDiscovery technology and costs. If you would like to discuss this topic further, please reach out to Casey at cvanveen@lighthouseglobal.com and Gordon at Gordon.Calhoun@lewisbrisbois.com.ediscovery-review; ai-and-analyticsediscovery-process, blog, spectra, law-firm, ediscovery-review, ai-and-analyticsediscovery-process; blog; spectra; law-firmcasey van veen

Whether it’s called DIY eDiscovery, SaaS eDiscovery, or self-service, spectra eDiscovery, one thing is clear—everyone in the legal world is interested in putting today’s technologies to work for them to get more done with less. It’s a smart move, given that many legal teams are facing an imbalance between needs and resources. As in-house legal budgets are being slashed, actual workloads are increasing.Now more than ever, legal teams need to ensure they’re choosing and using the right tools to effectively manage dynamic caseloads—a future-ready solution capable of supporting a broad range of case types at scale. Given the variety of options on the market, it’s understandable there’s some uncertainty about what to pursue, let alone what to avoid. Below, I have outlined guidance to help your legal team navigate the top three potential pitfalls encountered when seeking a self-service, spectra eDiscovery solution.1. Easy vs. PowerfulThere are a lot of eDiscovery solutions out there making bold promises, but many still force users to choose between ease of use and full functionality. While a platform may be simple to learn and navigate, it may fail to offer advanced features like AI-driven analysis and search, for example.Think of it like the early days of cell phones, when we were forced to choose between a classic brick-style device or a new-to-market smartphone. Older phones were easy to use, offering familiar capabilities like calling and text exchange, while newer smartphones provided impressive, previously unknown functionalities but came with a learning curve. With the advancement of technology, today’s device buyers can truly have it all at hand—a feature-rich mobile phone delivered in an intuitive user experience.The same is true for dynamic eDiscovery solutions. You shouldn’t have to choose between power and simplicity. Any solution your team considers should be capable of delivering best-in-class technology over one simple, single-pane interface.2. Short-Term Thinking vs. Long-Term Gains As organizations move to the seemingly unlimited data storage capacities of cloud-based platforms and tools, legal teams are facing a landslide of data. Even the smallest internal investigation may now involve hundreds of thousands of documents. And with remote working being the new global norm, this trend will only continue to grow. Legal teams require eDiscovery tools that are capable of scaling to meet any data demand at every stage of the eDiscovery process.When evaluating an eDiscovery solution, keep the future in mind. The solution you select should be capable of managing even the most complex case using AI and advanced analytics—intelligent functionality that will allow your team to efficiently cull data and gain insights across a wide variety of cases. Newer AI technology can aggregate data collected in the past and analyze its use and coding in previous matters—information that can help your team make data-driven decisions about which custodians and data sources contain relevant information before collection. It also offers the ability to re-use past attorney work product, allowing you to save valuable time by immediately identifying junk data, attorney-client privilege, and other sensitive information.3. Innovation vs. UpkeepThanks to the DIY eDiscovery revolution, your organization no longer has to devote budget and IT resources to upkeeping a myriad of hardware and software licenses or building a data security program to support that technology. Seek a trusted solution provider that can take on that burden with development and security programs (with the requisite certifications and attestations to prove it). This should include routine technology assessment and testing, as well as using an approach that doesn’t disrupt your ongoing work.As you’re asked to do more with less, the right cloud-based eDiscovery platform can ensure your team is able to meet the challenge. By avoiding the above pitfalls, you’ll end up with a solution that’s able to stand up against today’s most complex caseloads, with powerful features designed to improve workflow efficiency, provide valuable insights, and support more effective eDiscovery outcomes.If you’re interested in moving to a DIY eDiscovery solution, check out my previous blog series on self-service, spectra eDiscovery for corporations, including how to select a self-service, spectra eDiscovery platform, tips for self-service, spectra eDiscovery implementation, and how self-service, spectra eDiscovery can make in-house counsel life easier. ediscovery-review; ai-and-analyticsself-service, spectra, ediscovery-process, corporation, prism, blog, spectra, corporate, ediscovery-review, ai-and-analyticsself-service, spectra; ediscovery-process; corporation; prism; blog; spectra; corporatelighthouse

The approach of a new year is often a good time to step back and take stock of the eDiscovery industry, so that we can be better prepared to move forward. One of the most dramatic changes over the past few years has been the seismic shift across the legal and corporate data landscapes. That shift has slowly been expanding the concept of eDiscovery beyond a single-litigation focus, to encompass data governance, data privacy and security, and an overall more holistic, strategic approach to review and analysis.As we prepare to move forward in this brave new world, it’s important to understand how those industry changes affect the traditional framework of the eDiscovery process: the Electronic Discovery Reference Model (EDRM). Recently, I was lucky enough to join a panel of industry experts, including Microsoft’s EJ Bastien, TracyAnn Eggen from CommonSpirit Health, and Lighthouse’s Sarah Barsky-Harlan, to dive deeper into that specific issue. Together, we tackled questions like: Does the EDRM still apply in today’s more complex eDiscovery environment? If so, how is the evolving data and eDiscovery landscape reshaping how organizations and law firms think about the EDRM? How can the EDRM be used to meet today’s more complex communication, data, and business challenges?Below are some of the key themes and ideas that emanated from that discussion: A Brave New Data World: Dynamic Changes in eDiscoverySince its inception, the EDRM has been the industry’s standard approach to the eDiscovery process (i.e., identification, collection, processing, review, analysis, and production of electronically stored information (ESI)). However, what we’re seeing today is that organizations and law firms now must think about eDiscovery in much broader terms than that traditionally very linear method. There are three primary reasons for this change:New cloud-based and Software as a Service (SaaS) systems: Enterprise systems are not nearly as controlled by the underlying organization as they used to be. Even five years ago, IT departments could more closely manage what software was installed, as well as when, how, and what upgrades were rolled out. Now those updates and installations are managed by cloud providers, with upgrades rolling out on an almost weekly basis – often with no notice to the organization. All those changes have downstream eDiscovery impacts, which must be dealt with at each stage of the EDRM process.New data formats: Data is no longer structured in the traditional document “family” of an email parent with attachment children. The shift to chat and collaboration platforms within organizations means that communications and workflows generate more data across multiple data sources and are much more fluid and informal. For instance, instead of an employee working on a static document saved on a desktop and then passing that document back and forth to co-workers via email, those employees may work on that document together while it’s saved on a cloud-based collaboration platform, chat about it via an in-office chat application, post updates on it via the collaboration tool channel, as well as email copies back and forth to each other. This means counsel must analyze how relevant data ties together and analyze the relationships between data sources in order to understand the full story of a communication during an investigation or litigation.New capabilities with eDiscovery technology: There are many new types of capabilities that are native to enterprise systems, as well as new types of analytics and artificial intelligence (AI) that can handle more data at scale. These new capabilities are allowing case teams to leverage past data on new cases and get to key data more quickly in the EDRM process. The Impact: How Those Changes Affect the EDRM FrameworkThinking of the EDRM as a monolithic linear process that flows straight from beginning (collection) to end (production) does not fit the way eDiscovery takes place in practice anymore. There is a world of complexity within each step of the EDRM – one that is highly dependent on the data source. And the decisions made along the way for each data source at each new step will impact what happens next – often in a non-linear fashion: Sometimes that next step will send practitioners back to collection again, because they found another data source during review. Sometimes review takes place simultaneously with collection or processing phases, depending on the data source and those newer capabilities discussed above. In short, the old model of collecting all data, exporting it all, and then reviewing it all, in large chunks, one step at a time, is no longer applicable nor practical.Instead, a “mini-EDRM” framework might make more sense, where organizations prepare workflows for the preservation, collection, processing, and review of each particular data source. Thinking of the EDRM in this way also helps the framework stay relevant and future-proof as practitioners deal with the sea-change happening across our data landscape. Practitioners need to be agile enough to handle new data sources as they pop up, for each step of the EDRM process, and then be prepared to do it all over again when someone in a deposition mentions another new data source, and to adapt it when something changes in the data source. A mini-EDRM framework would help organizations and practitioners better meet those challenges.The EDRM and Data-in-PlaceAs noted above, the eDiscovery process is now much broader and has much more of an impact on organizational information governance and data-in-place than ever before. This presents an opportunity to use learnings from across the EDRM to more effectively manage data “to the left” of that traditional process. For example, if a particular data source was problematic during review, that information can be disseminated at the organizational level and help inform how that source is used within the organization moving forward. Or if practitioners notice a large volume of irrelevant data during review that shouldn’t exist in the system at all, that information can be used to redraft document retention policies. In this way, eDiscovery (and the EDRM framework) can now be a force for change over the entire organization.Thinking Beyond a Single MatterIn today’s more dynamic and voluminous data landscape, the work we did in the past is more valuable than ever before and it can be used to inform and impact current processes across the EDRM.This can come in the form of people and institutional knowledge: experienced and consistent staff and outside partners are an invaluable resource. These organizational experts can use their understanding and experience with an organization’s past matters, system architecture, data sources, workflows etc. to improve eDiscovery efficiency and solve current problems more effectively. It can also come in the form of technology: when the EDRM first evolved, data analytics were a much heavier lift. The process and tools were expensive and the amount of data that they could be applied to was much smaller than today. Advancements in AI capabilities now allow us to analyze much larger volumes of data with much more accurate results. Thus, this newer, advanced AI technology is now capable of leveraging the goldmine of millions of previous decisions made by attorneys on an organization’s past matters. That work product is baked into the data, and advanced AI can use it to make more accurate decisions on current data at a much larger scale than ever before.Tips to Keep the EDRM Applicable in an Evolving Data LandscapeStrive to retain institutional knowledge across matters: The constantly evolving eDiscovery landscape makes continuity and retaining institutional knowledge incredibly important. Starting from scratch each time you confront a new data source or problem along the EDRM is no longer practical with today’s diversified and larger data volumes. Work to cultivate valuable partners and staff who will work to understand your organization’s data architecture, as well as the eDiscovery workflows that are effective within your environment.Lean on your peers: Chances are, if you’re facing a problem with a challenging data source at one stage of the EDRM, someone in your peer group has also faced the same or a similar problem. Don’t be afraid to reach out and ask folks to benchmark. Peer experience can help each practitioner learn and move forward, solving challenging industry problems along the way.Open the lines of communication: Because the EDRM process is much more iterative and each step impacts other steps, it is incredibly important that the people working on those steps do not work in silos. Everyone should know the downstream impacts of their decisions and workflows.Test… and test again: Employ a testing framework to test the impact of eDiscovery workflows on the underlying platforms, and then have a feedback loop to apply changes. This will ensure your eDiscovery program is forward-thinking, as opposed to reactive. Automate where possible: When striving for repeatable, defensible eDiscovery processes, predictability is key. And automation, when feasible, is a great way to achieve that predictability. Automating workflows across the EDRM will not only help improve efficiency and lower costs, it will also help minimize risk and keep your eDiscovery program defensible.information-governance; ediscovery-review; chat-and-collaboration-datacloud, analytics, information-governance, ediscovery-process, blog, information-governance, ediscovery-review, chat-and-collaboration-data,cloud; analytics; information-governance; ediscovery-process; bloglighthouse

Let’s begin by setting the stage. You’ve evaluated the ways a self-service, spectra eDiscovery solution could benefit your organization and determined the approach will help you boost workflow efficiency, free up internal resources, and reduce eDiscovery practice and technology costs. You’ve also researched how to ideally implement a solution and armed yourself with strategies to build a business case and overcome stakeholder objections that may arise.You’re now ready to move on to the next step in your organization’s self-service, spectra eDiscovery journey: selecting the right solution provider. When it comes to selecting a solution provider, one size does not fit all. Every organization has different eDiscovery needs—including yours—and those needs evolve. From how attorneys and eDiscovery teams are structured within the organization and their approach to investigations and litigations, to the types of data sources implicated in those matters and how those matters are budgeted—there’s a lot to be considered.The self-service, spectra solution you choose should be able to adapt to your changing needs and grow with your organization. Below, I’ve outlined four key considerations that will help you select a fitting self-service, spectra solution for your organization.1. Is the solution capable of scaling to handle any matter? ‍It’s important to select a self-service, spectra eDiscovery solution capable of efficiently handling any investigation or litigation that comes your way. A cloud-based solution can easily, swiftly scale to handle any data volume.You’ll also want to ensure your solution can handle the type of data your organization routinely encounters. For example, collecting, processing, and reviewing data generated by collaborative applications like Microsoft Teams may require special tools or workflows. The same can be said for data generated by chat messages or cellphone data. Before selecting a self-service, spectra solution, you’ll benefit from outlining the types of data your organization must handle and asking potential solution providers how their platform supports each.Additionally, you may be interested in the ability to move to a full-service model with your provider, should the need arise. With scalable service, your team will have access to reliable support if a matter become too challenging to manage in house. With a scalable solution bolstered by a flexible service model, your organization can bring on help as needed, without disruption. 2. Does the solution drive data reduction and review efficiency across the EDRM?‍Organizational data volumes are increasing year after year—meaning even small, discrete internal investigations can quickly balloon into hundreds of thousands of documents. Collecting, processing, analyzing, and producing large amounts of data can be costly, complicated, time consuming, and may open up your organization to legal risk if the right tools and workflows are not in place.Look for a self-service, spectra solution capable of managing data at scale, with the ability to actively help your organization reduce its data footprint. This means choosing a provider that can offer expert guidance around data reduction techniques and tools. Ask potential solution providers if they have resources to address the cost burden of data and mitigate risk through strategies like defensible data collections, effective search term selection, or crafting early case assessment (ECA), and technology assisted review (TAR) workflows.The provider should also be able to deliver technology engineered to reduce data resource draw, like processing that allows access to data faster, tools to cut down on hosted review data volume, and AI and analytics that provide the ability to re-use attorney work product across multiple matters. In short, seek a self-service, spectra solution that gives your organization the ability to defensibly and efficiently reduce the amount of costly human review across your organization’s portfolio. 3. Will the solutions’ pricing model align to your organization’s changing needs? Your organization’s budget requirements are unique and will likely change over time. Look for a solution provider that can change in accord and offer a variety of pricing models to fit your budgetary requirements. Ask prospective providers if they are able to design pricing around your organization’s expectations for utilization. Modern pricing models can be flexible yet predictable to prevent unexpected charges or overages, and ultimately align to your organization’s financial needs.4. Is the solution’s roadmap designed to take your organization into the future? When selecting a self-service, spectra solution it’s easy to focus on your current needs, but it’s equally important to consider what a self-service, spectra solution provider has planned for the future. If a vendor is not forward thinking, an organization may find itself being forced to used outdated technology that’s not able to take on new security challenges or process and review emerging data sources.Pursue a provider that demonstrates the ability to anticipate market trends and design solutions to address them. Ask potential providers to articulate where they see the market moving and what plans they have in place to update their technology and services to reflect what’s new. It can be helpful to question if a provider’s roadmap aligns to your organization’s direction. For example, if you know your company is planning to make a systematic change, like moving to a bring your own device (BYOD) policy or migrating to the cloud, you’ll want to confirm the self-service, spectra solution can support that change. Asking these types of questions before selecting a provider will guarantee the solution you choose will be able to grow with both your organization and the eDiscovery industry as a whole. With awareness and understanding of the true potential offered in a self-service, spectra solution, you can ultimately choose a provider that will help you level up your organization’s eDiscovery program. ediscovery-review; ai-and-analyticsself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; bloglighthouse

This eDiscovery Day, the day that focuses on educating industry professionals around growing trends and current challenges, we are excited to announce that season two of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution, is now live.Co-hosts, Bill Mariano and Rob Hellewell, are back for season two of Law & Candor with six easily digestible episodes that cover a range of hot topics from cybersecurity to privilege tools. This dynamic duo, alongside industry experts, discuss the latest topics and trends within the eDiscovery, compliance, and information governance space as well as share key tips for you and your team to take away. Check out the latest season's lineup below:Bridge the Gap: Innovative Ways to Enable eDiscovery Collaboration Between Legal and ITThe Privilege in Leveraging Privilege Review ToolsData Preservation in the World of Ephemeral Data, Mobile Devices, and Other New Challenges in Forensic TechnologyCybersecurity in eDiscovery: Protecting Your Data from Preservation through ProductionWould a No-Deal Brexit Change How We Handle Cross-Border Collections in Europe?Understanding and Creating Effective and Best eDiscovery Practices for G-SuiteEpisodes are created to be short and bingeable so that you can listen on the platform of your choice with ease. Check them out now or bookmark them to listen to later.Follow the latest updates on Law & Candor and join in the conversation on Twitter. Catch up on season one today.For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewgdpr, privilege, cybersecurity, ediscovery-process, cross-border-data-transfers, blog, ediscovery-review,gdpr; privilege; cybersecurity; ediscovery-process; cross-border-data-transfers; bloglighthouse

AI & Analytics

eDiscovery is currently undergoing a fundamental sea change, including how we think about data governance and the EDRM. Linear review and older analytic tools are quickly becoming outdated and unable to handle modern datasets, i.e., eDiscovery datasets that are not only more voluminous than ever before, but also more complicated – emanating from an ever-evolving list of new data sources and steeped in variety of text and non-text-based languages (foreign language, slang, emojis, video, etc.).Fortunately, technological advancements in AI have led to a new class of eDiscovery tools that are purpose built to handle “big data.” These tools can more accurately identify and classify responsiveness, privileged, and sensitive information, parse multiple formats, and even provide attorneys with data insights gleaned from an organization’s entire legal portfolio.This is great news for legal practitioners who are faced with reviewing and analyzing these more challenging datasets. However, evaluating and selecting the right AI technology can still present its own unique hurdles and complexities. The intense purchasing process can raise questions like: Is all AI the same? If not, what is the difference between AI-based tools? What features are right for my organization or firm? And once I’ve found a tool I like, how do I make the case for purchasing it to my firm or organization?These are all tough questions and can lead you down a rabbit hole of research and never-ending discussions with technology and eDiscovery vendors. However, the right preparation can make a world of difference. Leveraging the below steps will help you simplify the process, obtain answers to your fundamental questions, and ultimately select the right technology that will help you overcome your eDiscovery challenges and up level your eDiscovery program.1. Familiarize Yourself with Subsets of AI in eDiscoveryNewer AI technology is significantly better at tackling today’s modern eDiscovery datasets than legacy technology. It can also provide legal teams with previously unheard-of data insights, improving efficiency and accuracy while enabling more data-driven strategic decisions. However, not all technology is the same – even if technology providers tend to generally refer to it all as “AI.” There are many different subsets of AI technology, and each may have vastly different capabilities and benefits. It’s important to understand what subsets of AI can provide the benefits you’re looking for, and how those different technology subsets can work together. For example, Natural Language Processing (NLP) enables an AI-based tool to understand text the same way that humans understand it – thus providing much more accurate classifications results – while AI tools that leverage deep learning technology together with NLP are better able to handle large and complex datasets more efficiently and accurately. Other subsets of AI give tools the ability to re-use data across matters as well as across entire legal portfolios. Learning more about each subset and the capability and benefits they can provide before talking to eDiscovery vendors will give you the knowledge base necessary to narrow down the tools that will meet your specific needs. 2. Learn How to Measure AI ROIAs a partner to human reviewers, advanced AI tools can provide a powerful return on investment (ROI). Understanding how to measure this ROI will enable you to ask the right questions during the purchasing process to ensure that you select a tool that aligns with your organization or law firm’s priorities. For example, if your team struggles with review accuracy when utilizing your current tools and workflows, you’ll want to ensure that the tool you purchase is quantifiably more accurate at classifying documents for responsiveness, privilege, sensitive information, etc. The same will be true for other ROI metrics that are important to your team, such as lower overall eDiscovery spend or increased review efficiency.These metrics will also help you build a strong business case to purchase your chosen tool once you’ve selected it, as well as a verifiable way to confirm the tool is performing the way you want it to after purchase.3. Come Prepared with a List of QuestionsIt’s easy to get swept up in conversations about tools and solutions that end without the metrics you need. A simple way to control the conversation and ensure you walk away with the information you need is to prepare a thorough list of questions that reflect your priorities. Also be sure to have a method to record each vendor’s response to your questions. A list of standard questions will keep conversations more productive and provide a way to easily contrast and compare the technology you’re evaluating. Ensure that you also ask for quantifiable metrics and examples to back up responses, as well as references from clients. This will help you verify that vendor responses are backed by data and evidence.4. Know the Pitfalls of AI Adoption—and How to Avoid ThemIt won’t matter how much you understand AI capabilities, whether you’ve asked the right questions, or whether you understand how to measure ROI, if you don’t know how to avoid common AI pitfalls. Even the best technology will fail to return the desired results if it’s not implemented properly or effectively. For example, there are some workflows that work best with advanced AI, while other workflows may fail to return the best results possible. Knowing this type of information ahead of time will help you get your team on board early, ensure a smooth implementation, and enable you to unlock the full potential of the technology.These tips will help you better prepare for the AI purchasing process. For more information, be sure to download our guide to buying AI. This comprehensive guide offers a deep dive into tips and tactics that will help you fully evaluate potential eDiscovery AI tools to ensure you select the best tool for your needs. The guide can also be used to reevaluate your current AI and analytic eDiscovery tools to confirm you’re using the best available technology to meet today’s eDiscovery challenges.lighting-the-way-for-review; ai-and-analytics; ediscovery-review; lighting-the-path-to-better-review; lighting-the-path-to-better-ediscoveryreview, ai-big-data, blog, ai-and-analytics, ediscovery-reviewreview; ai-big-data; blogai-analyticssarah moran

A good discovery strategy goes beyond complying with production obligations. When preparing for discovery matters, law firms and legal corporate departments most often focus on developing a compliant and cost-effective responsive review capability with the appropriate expert personnel, technology, and workflows. After all, once in place, a tested responsive review capability can provide legal counsel the cost predictability, security, and control needed to focus on legal strategy early on in large litigation matters.Yet, while it is necessary to develop a reliable in-house or managed responsive review capability for document-intensive litigation, being ready for the demands of modern day discovery extends beyond complying with production obligations. Most notably, the work of fact-finding often continues well after production, and introduces its own unique complexities and opportunities requiring special tactical attention.Responsive review and finding key documents require different workflows. There is a substantive difference between identifying what is responsive for production versus honing in on the key information that ultimately decides a case or investigation. While responsive review focuses on compliance with negotiated and documented requests for production, targeted fact-finding focuses on legal hypothesis-testing and story development in an actively changing and open-ended context.As such, the mix of skills, technologies, and workflows required for responsive review does not necessarily extend beyond the production phase. Honing in on key documents and dispositive information buried in large data sets requires tactical agility and adaptation that efficient responsive review approaches limit by design in order to achieve production compliance at scale.Targeted fact-finding requires an iterative process and an expert team.A common need shared across fact-finding case teams is quick identification of key documents to help develop a robust and coherent fact-based narrative. Rather than casting a broad net to look for similarities among documents as you would do in a responsive review, fact-finding teams require an ability to sleuth through large data sets in order to identify key players, reveal hidden connections between them, and establish an overall picture and timeline of what happened.More specifically, rather than leveraging linear review workflows—even those optimized by technology-assisted review (TAR)—targeted fact-finding is best supported by iterative workflows in which attorneys and discovery experts deeply familiar with the case conduct tailored interrogations of the data to find the information that will best help with developing the case team’s understanding of the matter.Broaden the notion of discovery for better preparedness. Limiting discovery preparedness to achieving scalable responsive review gives short-shrift to developing a capacity for targeted fact-finding. Broadening the notion of discovery preparedness to include fact-finding means reassessing your discovery capabilities beyond production-oriented questions, such as what number of documents will require eyes-on review or what level of accuracy can be achieved. It makes sense to make more qualitative, legal expert-based assessments such as: Have we been able to uncover the full extent of the relevant fact pattern? How confident are we that we have connected the dots regarding who acted improperly and who had knowledge of it?To answer these sorts of questions, senior members of the litigation team need to be actively informed at a granular level regarding fact-finding approaches and outcomes. A robust and effective fact-finding function can provide a critical advantage in not only witness and trial preparation, but also in early case assessment, internal investigations, and government subpoenas, increasingly important discovery contexts to consider in light of increased regulatory, shareholder, and public scrutiny of corporate fraud and wrong-doing.For more on fact-finding, see: eDiscovery for Investigations: Different Goal, Different Approach. ediscovery-reviewblog, ediscovery-reviewbloglighthouse

The overall impacts of COVID-19 are widespread and still very uncertain. The Law & Candor podcast team wanted to come together with this special edition to reflect on the current impacts and how they are affecting those the legal space and beyond.In this special edition, co-hosts Bill Mariano and Rob Hellewell, are joined by Lighthouse’s CEO, Brian McManus, to discuss his take on the industry impacts of COVID-19. The group discusses shifts in company priorities, common themes being heard throughout the industry, as well as the downstream implications this pandemic could have on the legal space by answering the following key questions:What are key company priorities?What are current employee safety priorities and items to be aware of?What is the industry saying?What will be the lasting impact of COVID-19 on the legal space?In conclusion, they share top takeaways from the episode. If you are interested in listening, click here. To join the conversation on Twitter, click here.Catch up on past seasons by clicking the links below:‍Season 1Season 2Season 3For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, cloud-security, blog, ediscovery-review,cloud; self-service, spectra; cloud-security; bloglighthouse

In a world of ever-increasing and evolving self-service, spectra models (think Amazon Go, fast food self-order kiosks, or even the self-service, spectra check in and check out at hotels), it’s no wonder the eDiscovery industry is headed in the same direction. In the last few years, new and improved SaaS eDiscovery tools have exploded onto the scene as corporations and law firms have started to embrace a self-service, spectra approach for executing the discovery work associated with both internal investigations and proper legal matters.As the historically risk-averse legal world has been slow to get on board with self-service, spectra eDiscovery models, you’re likely to still encounter objections if you ask your team to take the leap and invest in a self-service, spectra eDiscovery tool. Below, I outline the common objections I have encountered to self-service, spectra models and how you can overcome them by sharing some key value differentiators of on-demand eDiscovery tools that should persuade your team to embrace these new offerings and leave the antiquated, expensive on-prem solutions for good.Data Security Risks – The first, and potentially biggest, objection to the adoption of a self-service, spectra program often centers on concerns over data security and the associated risk. Corporations and law firms alike are concerned that self-service, spectra means cloud-based and as a result a lack of security and an increased exposure to risk. Historically, companies have been more comfortable with on-prem eDiscovery solutions because they have viewed that as meaning that they had complete control of their data without having to rely on a vendor or worry about their data being commingled with other clients’ data.However, with that control comes with a lot of risk, which can be greatly minimized by having a SaaS vendor that offers a private cloud. This private cloud can be a perfect solution because they do not have the same security concerns that are involved with the public cloud. When vetting potential SaaS partners, make sure to look for those that carry SOC 2, ISO, and HIPAA security certifications to ensure that your providers are staying up to speed on the latest security requirements.Steep Learning Curves – Oftentimes people I chat with will associate self-service, spectra with steep learning curves and lots of tedious training. This isn’t always the case.Although this may be true with some self-service, spectra solutions, but it’s important to note that not all platforms are created equal. Some solutions provide extensive functionality with a complex, hard to use, difficult to understand interface, while others strive to provide a simple interface often at the expense of functionality, while a rare few bridge the two finding that perfect blend of robust functionality and ease of use. Start the assessment into usability by understanding the true training required for the solutions you are evaluating, weigh the functionality vs. your team’s needs and skillset, talk to other users who have already adopted the solutions to validate the actual training lift, and finally showcase those findings with your team.Lack of Support – A common objection to self-service, spectra solutions is that they lack on-demand support and access to additional training and help if needed. Today’s lawyers and eDiscovery managers need to move through matters quickly and efficiently, and without access to readily-available help it frequently stalls the matter’s progress significantly. When looking into self-service, spectra solutions, choose one that offers on-demand support when, where, and how you need it. There are companies out there that offer this blend of autonomous control and augmented support and it is an easy objection to overcome if you can showcase that to your team. Minimal Flexibility – Frequently there is a hesitation to move forward with self-service, spectra due to the fear of getting stuck managing a matter in a self-service, spectra tool that could become large and/or complex and require additional help/support outside of your team’s capabilities or availability.Like I mentioned above, select a tool that also offers expert support. You can ease your team members’ worries by sharing that some self-service, spectra tools offer the ability to move from self-service, spectra to full service support when needed and scale up or down quickly.Too Pricey – The last major objection that I want to address is around the fact that many still believe self-service, spectra tools are too pricey and do not offer the ROI they need to see in order to make the switch.However, in an on-prem, behind the firewall model, there often are large up-front costs to purchase and install the technology as well as continued maintenance costs during the life of the product. In addition to the hardware costs there is the increased headcount necessary to support these platforms 24x7. With SaaS, the vendor purchases and maintains the software, as well as manages ongoing costs like upgrades and licensing. Managing an IT infrastructure and maintaining servers for eDiscovery data is a big cost for law firms and corporations often with no cost recovery mechanism. This cost burden can be greatly minimized with a SaaS solution.These top objections often come up in conversations around the adoption of a new self-service, spectra solution and I hope this blog has better prepared you to address them as you work to get your team on board. Feel free to reach out to further discuss these or other objections you may be facing at bthompson@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, blog, ediscovery-review,cloud; self-service, spectra; blogbrooks thompson

In this two-part series, I interview Gordon J. Calhoun, Esq. of Lewis Brisbois Bisgaard & Smith LLP about his thoughts on the state of eDiscovery within law firms today, including lessons learned and best practices to help attorneys overcome their trepidation of electronic discovery and build a better litigation practice. This second blog focuses on how attorneys within law firms can save their clients money, achieve better outcomes, and gain more repeat business once they overcome common misconceptions around eDiscovery.You mentioned earlier that you think attorneys who try to shoehorn volumes of electronic data into older workflows developed for paper discovery will likely cause attorneys to lose clients. Can you explain how? Sure. My point was that non-technological workflows often pop into the minds of attorneys because they are familiar, comfortable approaches to responding to document requests. Because they are familiar and can be initiated immediately, there is a great temptation to jump in and avoid planning and employing the expertise essential for an optimal workflow. Unfortunately, jumping in without much planning produces a result that is often unnecessarily costly to clients, particularly if the attorneys employ in-house resources (which are usually several times more costly than outsourced staff). In-house resources often regard document review and analysis as an undesirable assignment and have competing demands for their time from other projects and cases. This can result in unexpected delays in project completion and poor work product (in part because quality degrades when people are required to perform tasks they dislike). The end result is untimely, lower quality, and more costly than anticipated, which will ultimately cost the attorney their client.Clients will always gravitate towards the professional who can deliver a better, more cost-effective, and more efficient solution while avoiding motion expenses. That means that attorneys who are informed enough to use technology to save clients money on multiple cases are going to earn the trust and confidence of more and more clients. And that is the answer to the question as to what’s in it for the professional if he or she takes the time to learn about or partners with someone who already knows eDiscovery.Well, coming from a legal technology company, I agree with that sentiment. But we also tend to see attorneys from the other end of the spectrum: lawyers who understand the benefits advanced eDiscovery technology can provide, but avoid it because of fears around overhead expense and surprise fees. Have you seen this within your own practice? If so, how do you advise attorneys who may have similar feelings? I experience the same thing and, again, this type of thought process is completely understandable. When eDiscovery technologies were comparatively new, they seemed disproportionately expensive. The cost to process a GB of data could exceed $1,000, hosting charges ran into the many tens of dollars per month and there were no analytics to expedite review. When the project management service was in its infancy, too many of those providing services simply followed uninformed instructions from counsel. An instruction to process data was not met with inquiries as to whether all data collected should be processed or if an alternative should be explored when initial analysis indicated the data expansion would be unexpectedly large. Further, early case assessment (ECA) strategies utilizing only extracted text and metadata were years in the future. The only saving grace was that data volumes were miniscule compared to what they are today. But that was not enough to prevent widespread reports about massive eDiscovery vendor bills. As you might suspect, the problem was not so much the technology or even the lack thereof as it was the failure to spend the time to develop an appropriate workflow and manage the eDiscovery process so the results were cost effective. Any tips on how attorneys can overcome the remnant fear of eDiscovery “sticker shock”?This challenge can be met by research, planning, and negotiation: research into the optimal technologies and which providers are equipped to provide them, planning an appropriate workflow, and negotiation with eDiscovery platform providers to customize the offerings to the needs of your case. If you have the aptitude, consider investing some time and doing some research about eDiscovery solutions that provide predictable, transparent prices outside of the typical hourly and per-GB fee structure. A good eDiscovery platform provider should work with you to develop a fee arrangement that makes sense for your caseload and budget. There is no reason why even a small firm or individual practitioner cannot negotiate subscription-based or consumption-based fees for eDiscovery solutions the same way that forward thinking serial litigants like large corporations and insurers have. The pricing models exist and there is no reason they cannot be scaled for users with smaller demands. Under this type of arrangement, there will be no additional costs or surprise fees, which in turn will allow any practitioner to pass that price predictability on to his or her clients. Ultimately, this lower cost, increased predictability, and efficiency will enable an attorney to grow his or her book of business with repeat customers and referrals.So, if an attorney is able to negotiate an alternative fee arrangement with a legal technology provider, is that the end of the problem? Should that solve all an attorney’s eDiscovery concerns? It’s a start – but no. Even with a customized eDiscovery technology solution, part of the concern for most attorneys is the magnitude of the effort required to respond to discovery requests. On one hand, they’re faced with document requests fashioned by opposing counsel fearful of missing something that might be important unless they are massively overinclusive. They ask for each, every, and all documents and any form of electronic media that involves, concerns, or otherwise relates to 30, 50, 100, or more discrete topics. On the other hand, the attorney must reconcile this task of preserving, identifying, collecting, processing, analyzing, reviewing and producing ESI in a manner that complies with the applicable discovery laws or case specific discovery orders… all under what may be a modest budget approved by the client. This is where experience (or guidance from an experienced attorney), as well as a good eDiscovery technology provider can be a huge help. The principle that underlies a solution to the conundrum as to how to manage an overly broad discovery request with a limited budget is: proportionality. Emphasizing this principle is a major focus of the 2015 amendments to the FRCP. Got it. I think the logical follow up question to that answer is: how can attorneys attain “proportionality” in the face of ridiculous discovery requests (while also not exceeding the limited amount the client is prepared to spend)?The key to balancing these conflicting demands is insisting upon proportionality early and often. The principle needs to be addressed at a granular level with a robust understanding of the client’s data that will be the subject of opposing counsel’s discovery requests. For example, the number of custodians from whom data should be collected should not be a laundry list of everyone who might have knowledge about the issues in the case. Rather, counsel should be focused on the key players and how much data each has. The volume of data that counsel can afford to collect, process, analyze, review, and produce should depend largely on what the litigation budget is, which in turn should generally depend on the amount in controversy. There are exceptions to this rule of thumb, but this approach to proportionality needs to be raised during the initial meetings of counsel in advance of the first case management order. If the case is one where the general rule does not apply (e.g., a matter of public interest), the client should be informed immediately because the cost of litigation is likely to be disproportionate to its economic value and the client may prefer to have some other entity litigate the issue. An experienced attorney should be involved in this meet and confer process because the results of these early efforts are likely to create the foundation and guard rails for the remainder of the case. Any issues that are left to future negotiation create a potential for costs to balloon in unexpected ways. Can you dive a bit deeper into proportionality at different phases of the discovery process? Is there anything else attorneys can do to keep cost from ballooning before data is collected?As I alluded to a moment ago, one key to controlling scope and cost is to negotiate a limited number of custodians that is proportional to the value of the case. In larger cases, it will be appropriate to create tiers of custodians and limit progression into the lower tier custodians to those instances where opposing counsel make a good faith showing that additional discovery is necessary based on identifiable gaps of information rather than upon speculation about what might be found if more discovery is permitted. If opposing counsel doesn’t agree to a limited number of custodians or staging discovery in larger cases, counsel would be well advised to prepare a case management order or a protective order to keep the scope of discovery proportional to the value of the case. To be successful, an attorney and his or her technology provider will have to understand the data in the client’s possession and provide metrics and costs associated with the alternative approaches to discovery.Great advice. How about once data is collected and analysis has begun? How can attorneys keep costs within budget once they've got the data into an eDiscovery platform?Attorneys should continue to budget proportionally throughout the case. This budget will obviously include the activities identified by the Electronic Discovery Reference Model (EDRM). The EDRM provides a roadmap to respond to opposing parties’ discovery requests: identifying those documents that are needed to make our case, regardless of whether opposing parties requested them; winnowing the documents identified to a subset for use in deposition preparation; drafting potentially dispositive motions; and preparing for mediation; and, if necessary, preparing for inclusion on the trial exhibit list. The EDRM was designed to help attorneys identify documents that are reasonably calculated to lead to the discovery of admissible evidence or relate to claims and defenses asserted in the case. In a case with 100,000 documents collected, that could easily be 10,000 to 15,000 documents. The documents considered for use in depositions, law and motion, or mediation will be a small fraction of that amount and will include a similar culling of those documents produced by other parties and third parties. Only a fraction of those will make it onto the trial exhibit list and fewer will be presented to the trier of fact.Responding to discovery and preparing the case for resolution are two very different tasks and the attorney’s budget must accommodate these two different activities. Monies must be reserved for other written discovery requests, both propounding them and responding to them, and for depositions. Because the per-GB prices for these activities are predictable, an attorney and technology provider should be able to readily determine how much information they can afford to collect and put into the eDiscovery workflow. Counsel needs to be ready to share this information with opposing parties during the early meetings of counsel. But what happens when there is just a legitimately large amount of data, even after applying all the proportionality tactics you described earlier? Counsel should only agree to look at more data than that to which the parties originally agreed if opposing counsel can show good cause to incur that time and expense. If more data needs to be analyzed, the only reliable way to avoid busting the budget is to use AI to build on the document classification that occurred during the initial round of eDiscovery activities. Counsel should take advantage of statistically defensible sampling to determine the prevalence of responsive documents in the data and cut off analysis and review when a defensible rate of recall has been achieved. The same technologies should be employed to identify documents that should not be produced, e.g., those that are privileged or contain trade secrets unrelated to the pending litigation or other data exempt from discovery – enabling counsel to reduce the amount of expensive attorney review required on a given case.By proactively managing eDiscovery proportionality and leveraging all the efficiency that modern eDiscovery platforms provide (either by developing the necessary expertise to do so or associating with an attorney who does) – any lawyer will be able to handle any discovery request in a cost-effective manner.You mentioned choosing a database and legal technology provider. Do you have any advice for attorneys on how to choose the best one to meet their needs?I won’t weigh in on specifics, but I will say this: do the necessary research or consult with someone who has. In addition to investigating the various technologies available, counsel must become familiar with a variety of pricing models for delivery of the technologies needed to respond to eDiscovery requests. Instead of treating every case as an a la carte proposition, consider moving to a subscription-based self-service eDiscovery platform solution. This allows counsel savvy with the technology to control his or her cases within the platform and manage costs in a much more granular way than is possible when using a full-service eDiscovery technology provider, without incurring additional licensing, hosting, and technology fees. With a self-service solution, a provider hosts the data within their own cloud (and thus takes on the data security, hosting, and technology fees), while counsels gain access to all the current versions of eDiscovery tools to help manage the client’s costs. It will also allow counsel to customize the platform and automate workflows to meet his or her own specific needs, so that no one is spending time and money re-inventing the wheel with every new case. A self-service solution also comes with the added benefit of being immediately available from any web browser and gives counsel the ability to transfer data into platform at the touch of a button. (This means that when a prospective client asks whether you have a solution to handle the eDiscovery component of a case, the answer will always be an immediate “yes”).What happens if counsel does not feel ready to take on all eDiscovery responsibilities in a “self-service” model?If counsel is not ready to take on full responsibility for managing the eDiscovery process but still wants the cost-savings of a self-service model, find a technology provider that offers project management services and guidance that will act as training wheels until counsel is ready to navigate the process without assistance. There are also service providers who offer flexible arrangements, where large matters can be handled by their full-service team while smaller matters or investigations can remain “self-service” and be handled directly by counsel.Those are great tips, Gordon – I couldn’t have said it better myself. Any last thoughts for attorneys related to discovery and leveraging eDiscovery technology? Thank you, it’s been a pleasure. As for last thoughts, I think it would be this: in 2021, no attorney should fear responding to eDiscovery requests. Attorneys who still have that fear need to start asking, “If the data exists electronically, can I use technology to extract what I need less expensively than if I put eyeballs on every document?” The answer is almost always, “Yes.” The next question those attorneys should ask is, “How do I go about extracting the information I need at the lowest possible cost?” The answer to that question may be unique to each attorney, and this is where I recommend doing some up-front research and preparation to identify the best technology solution before you are looking down the barrel at a tight discovery deadline.Ultimately, finding the right technology solution will enable you to meet every discovery request with confidence and ultimately grow your book of business. If you would like to discuss this topic further, please reach out to Casey at cvanveen@lighthouseglobal.com and/or Gordon Calhoun at Gordon.Calhoun@lewisbrisbois.com.ediscovery-review; ai-and-analyticsediscovery-process, blog, spectra, law-firm, ediscovery-review, ai-and-analyticsediscovery-process; blog; spectra; law-firmcasey van veen

We're excited to announce that season four of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution, is now available. Click the image below to binge season four now or keep scrolling for more details on the latest season. Co-hosts, Bill Mariano and Rob Hellewell, are back for season four of Law & Candor with six easily digestible episodes that cover a range of hot topics from cybersecurity to privilege tools. This dynamic duo, alongside industry experts, discuss the latest topics and trends within the eDiscovery, compliance, and information governance space as well as share key tips for you and your team to take away. Check out the latest season's lineup below:Emerging Data Sources: Get a Handle on eDiscovery for Collaboration Tools Myth Busters: The Managed Services Edition Legal Operations 101: Skills for SuccesseDiscovery Program Starter Pack: Uncover Key Ways to Build an Effective & Efficient eDiscovery ProgramManaging Cybersecurity in eDiscoveryTake the Mystery out of Machine Learning: Success Stories from Real-Life Examples and How Data Scientists Impact eDiscoveryEach episode is bingeable and available on your podcast platform of choice including Apple, Spotify, Stitcher, and Google. Follow the latest updates on Law & Candor by subscribing on the podcast home page and join in the conversation on Twitter. Catch up on past seasons by clicking the links below:‚ÄçSeason 1Season 2Season 3Special Edition: Impacts of COVID-19For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewcloud, cybersecurity, emerging-data-sources, cloud-security, tar-predictive-coding, ediscovery-process, legal-ops, managed-services, blog, ediscovery-review,cloud; cybersecurity; emerging-data-sources; cloud-security; tar-predictive-coding; ediscovery-process; legal-ops; managed-services; bloglighthouse

We are thrilled to announce the one-year anniversary of our Law & Candor podcast. One year, five seasons, and 30 episodes later, we are still here and wholly devoted to pursuing the legal technology revolution. Click the image to listen to season five now or scroll down for more details. Co-hosts Bill Mariano and Rob Hellewell are back for season five of Law & Candor with six easily digestible episodes that cover a range of hot topics from cloud migrations to managing DSARs. This dynamic duo, alongside industry experts, discuss the latest topics and trends within the eDiscovery, compliance, and information governance space as well as share key tips for you and your team to take away. Check out the latest season's line-up below:Achieving Information Governance Through a Transformative Cloud Migration Scaling Your eDiscovery Program: Self Service to Full Service Leveraging AI and Analytics to Detect PrivilegeEffective Strategies for Managing DSARsFacilitating a Smooth and Successful Large Review Project with Advanced AnalyticsTop Microsoft 365 Features to Leverage in Your eDiscovery ProgramEpisodes are created to be short and bingeable so that you can listen on the platform of your choice with ease. Check them out now or bookmark them to listen to later. Follow Law & Candor on Twitter to get the latest updates and join the conversation.Catch up on past seasons by clicking the links below:Season 1Season 2Season 3Season 4Special Edition: Impacts of COVID-19For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewcloud, information-governance, ai-big-data, blog, ediscovery-review,cloud; information-governance; ai-big-data; bloglighthouse

Season three of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution, is now available for your listening pleasure. Click the image below to binge season three now or keep scrolling for more details on the latest season. Law & Candor co-hosts, Bill Mariano and Rob Hellewell, have done it again. They have developed yet another riveting season of content by bringing on industry experts from AstraZeneca, Dentons, Dignity Health, Goulston & Storrs, GSK, and Lighthouse to discuss hot topics within the eDiscovery, compliance, and information governance space. Season three is filled with valuable takeaways, practical tips, and lots of banter along the way. See the season three episode lineup below:Tackling Big Data ChallengesData Privacy in a Post-GDPR World: Facing Regulators and Ensuring Compliance Through Rock-Solid Information Governance PracticeThe Future of On-Demand SaaS Software for Small Matters – A self-service, spectra Model StoryNew Efficiency Gains in TAR 2.0 and CMML RevealedHow Microsoft 365 and GDPR Are Driving a Proactive Approach to eDiscovery Across the GlobeeDiscovery Shark Tank - What’s Worth Your Investment in 2020?Each episode is bingeable and available on your podcast platform of choice including Apple, Spotify, Stitcher, and Google. Check them out now or bookmark them and listen later. Follow the latest updates on Law & Candor and join in the conversation on Twitter. Catch up on past seasons by clicking the links below:Season 1Season 2‍‍For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewmicrosoft, cloud, gdpr, self-service, spectra, data-privacy, ai-big-data, cloud-security, tar-predictive-coding, blog, ediscovery-review,microsoft; cloud; gdpr; self-service, spectra; data-privacy; ai-big-data; cloud-security; tar-predictive-coding; bloglighthouse

By Steven L. Clark, E-Discovery and Litigation Support Director, Dentons and John Del Piero, Vice President, LighthouseWhether your firm is just beginning to consider a move to a managed service eDiscovery model or you’re a managed service veteran, it is imperative to understand what makes this type of eDiscovery program model successful. After all, if you don’t know how to measure success, it will be difficult to know what to look for when selecting a provider, and equally as hard to monitor the quality of the services provided once you have selected one.However, measuring success can be complex. There are many different metrics that could be used to measure success and each may be of a varying level of importance to different firm stakeholders, as the priorities of these stakeholders will be determined by their particular role and focus. However, a successful managed service partnership can be based on a foundation of six core pillars. These pillars can be used as guideposts when evaluating whether a managed service partner will truly add value to a law firm’s eDiscovery process.Pillar 1: Access to Best-of-Breed Technology and Teams of Experts to Help Leverage ItA managed service partnership should always make a law firm (and its clients) feel like the best eDiscovery technology is right at their fingertips. But more than that, a successful managed service relationship should enable a law firm to stay technologically agile, while lowering technology costs.For example, if an eDiscovery tool or platform becomes obsolete or outdated, the firm’s managed service partner should be able to quickly move the firm to better technology, with little cost to the firm. In other words, in a successful managed service partnership, gone are the days where a litigation support team was stuck using an obsolete platform simply because the law firm purchased an enterprise license for that technology. Rather, the managed service partner should bear the cost burden of leveraging continuously evolving technology because the partner can easily spread that technological risk across its client base. In assuming this burden, the managed services partner ultimately provides law firms much greater flexibility in terms of leveraging the most appropriate technology to meet their clients’ needs.In addition to simply providing access to the best technology, a successful managed service partnership should also provide teams of experts who are wholly dedicated to helping law firms leverage that technology for optimal impact. These experts should be continuously vetting new applications and technology upgrades, enabling litigation support teams to stay up to date on evolving applications and tools. These teams will also be able to create and test customized workflows that enable law firms to handle how data flows through technically robust collaborative platforms like Microsoft Teams or Slack, as well as keep firms apprised of any updates to cloud-based platforms that may affect existing eDiscovery workflows.This type of devoted technological expertise and guidance can provide firms a significant competitive boost, as internal litigation support teams rarely have the resources available to devote staff solely to testing new technology and building customized workflows.Pillar 2: A Scalable and More Diversified eDiscovery Team In comparison to a traditional law firm litigation support team which, naturally, is somewhat static in size, a successful managed service relationship allows law firm teams to quickly and seamlessly scale up or down, depending on case needs. For example, when a large matter comes in, a managed service provider should have the ability to quickly pull a project manager in to help manage the case while the internal law firm team still retains day-to-day control of the matter. This alleviates the firm from having to choose between hiring additional staff (only to be faced with too big of a team once the larger matter ends) or outsourcing the case to an external, inflexible eDiscovery provider (where the firm may be unable to retain full control of the matter and will undoubtedly have to adapt to different processes and workflows).A managed service partner’s bench should also be deep, allowing a law firm to pull from a diverse pool of expertise. Whether the law firm needs a review workflow expert or a processing expert, an analytics expert or a migration and normalization expert, a quality managed service provider should be able to swiftly provide someone who knows the teams involved and has the qualifications and technological background to ensure that all stakeholders trust their expertise and guidance.Pillar 3: eDiscovery Expertise 24/7/365A managed service provider should not only provide law firms with top-notch eDiscovery expertise but also provide access to that expertise whenever it is needed. Unfortunately, most litigation support teams are all too familiar with the fact that eDiscovery is almost never a 9 to 5 job. The nature of litigation today means that a Monday production deadline involving a terabyte of data may be doled out by a judge on a Friday morning, or that data for a pressing production may arrive at 9:00 p.m. The list of eDiscovery off-hour emergencies is somewhat endless.Unfortunately, most internal litigation support teams at law firms are located in one geographic area (and therefore, one time zone), meaning that even when internal teams have the required expertise, they may not have those resources available when they’re needed.A quality managed service partner, however, will be able to provide resources whenever they are needed because it can structure its hiring and team assignments with team members located across multiple time zones. Access to full-time eDiscovery expertise and coverage enables law firms to swiftly handle any eDiscovery task with ease, with no permanent increase in staffing overhead.Pillar 4: Less Talent Acquisition RiskA successful managed service relationship should also significantly lower law firm risk related to talent acquisition and training. While hiring in today’s job climate may seem like a simple task, the cost of sufficiently vetting candidates and then providing the appropriate training can be incredibly time consuming and expensive.If law firm vetting misses a candidate red flag or even if a candidate just needs more training than expected, staffing costs and time expenses can skyrocket even further. For example, the task of having to substantially re-train a new hire from the ground up can take up the valuable time of other internal experts. In this way, even the most routine hire can often slow productivity and lower the morale of the entire internal team (at least in the short term) until the hire can be fully integrated into the department’s daily workflow.In a successful managed service relationship, however, the law firm can transfer those types of hiring and training risks directly to the provider. The managed service provider is already continuously evaluating, vetting, and training talent across different geographies in order to hire the best eDiscovery experts. Law firms can simply reap the benefit of this process by partnering with the service provider and leveraging that talent once the vetting and training process has been completed.Pillar 5: Lower Staffing Overhead To put it simply, all of the above means that moving to a managed service model should allow a law firm to significantly lower its overhead costs related to staffing and management. In addition to taking on the hiring risks, a managed service provider should also take on much of the overhead related to maintaining staff. From payroll, to benefits, to overtime costs, a quality managed service provider handles those costs and time expenses for their own on-staff experts, leaving the law firm free to reap the benefits of on-demand expertise without the staffing overhead costs.Pillar 6: Better Billing MechanicsMost law firms are not set up to bill eDiscovery services efficiently. eDiscovery billing has evolved over the last few years, and a quality managed service provider should be following suit and offering simplified, predictable cost models in order for law firms to pass that predictability on to their clients. This kind of simplified pricing enables all parties to understand exactly how much they are going to spend for the eDiscovery services provided. However, this billing structure differs significantly from the way traditional legal work is billed out, and most law firms’ billing infrastructures have not evolved to offer the same level of predictability or cost certainty. This is where a quality managed service provider can provide another benefit, by heavily investing its own resources into building out automated reporting, ticketing, and billing systems that can generate proformas and integrate into the firm’s existing billing systems.If a managed service provider can take care of these billing tasks, law firm teams can spend more time in furtherance of client work, rather than devoting resources into eDiscovery billing metrics and workarounds.SummaryAccess to and expertise in appropriate technology, flexible staffing models, lower overhead, and simplified pricing are the six pillars of a successful managed service partnership in a law firm setting. When all six of these pillars are in place, the managed service partnership will result in more satisfied internal and external law firm customers and an increasing caseload year after year. For more information or to discuss this topic, reach out to us at info@lighthouseglobal.com.legal-operations; ediscovery-reviewmanaged-services, blog, law-firm, legal-operations, ediscovery-reviewmanaged-services; blog; law-firmlighthouse

Legal professionals often take for granted that the eDiscovery software they leverage in-house must come with capability tradeoffs (i.e., if the production capability is easy to use, then the analytics tools are lacking; if the processing functionality is fast and robust, then the document review platform is clunky and hard to leverage, etc.).The idea that these tradeoffs are unavoidable may be a relic passed down from the history of eDiscovery. The discovery phase of litigation didn’t involve “eDiscovery” until the 1990s/early 2000s, when the dramatic increase in electronic communication led to larger volumes of electronically stored information (ESI) within organizations. This gave rise to eDiscovery software that was designed to help attorneys and legal professionals process, review, analyze, and produce ESI during discovery. Back then, these software platforms were solely hosted and handled by technology providers that weren’t yet focused entirely on the business of eDiscovery. Because both the software and the field of eDiscovery were new, the technology often came with a slew of tradeoffs. At the time, attorneys and legal professionals were just happy to have a way to review and produce ESI in an organized fashion, and so took the tradeoffs as a necessary evil.But eDiscovery technology, as well as legal professionals’ technological savvy, has advanced light years beyond where it was even five years ago. Many firms and organizations now have the knowledge and staff needed to move to a “self-service, spectra” eDiscovery model for some or all of their matters – and eDiscovery technology has advanced enough to allow them to do so. Unfortunately, despite these technological advancements, the tradeoffs that were so inherent in the original eDiscovery software still exist in some self-service, spectra eDiscovery platforms. Today, these tradeoffs often occur when technology providers attempt to develop all the technology required in an eDiscovery platform themselves. The eDiscovery process requires multiple technologies and services to perform drastically different and overlapping functions – making it nearly impossible for one company to design the best technology for each and every eDiscovery function, from processing to review to analytics to production.To make matters worse, the ramifications of these tradeoffs are much wider than they were a decade ago. Datasets are much larger and more diverse than ever before – meaning that technological gaps that cause inefficiency or poor work product will skyrocket eDiscovery costs, amplify risk, and create massive headaches for litigation teams. But because these types of tradeoffs have always existed in one form or another since the inception of eDiscovery, legal professionals still tend to accept them without question.But rest assured best-in-class technology does exist now for each eDiscovery function. The trick is being able to identify the functionality that is most important to your firm or organization, and then select a self-service, spectra eDiscovery platform that ties all the best technology for those functions together under one seamless user interface.Below are three key steps to prepare for the research and purchasing process that will help drastically minimize the tradeoffs that many attorneys have grown accustomed to dealing with in self-service, spectra eDiscovery technology. Before you begin to research eDiscovery software, you’ve got to fully understand your firm or organization’s needs. This means finding out what eDiscovery technology capabilities, functionality, and features are most important to all relevant stakeholders. To do so:Talk to your legal professionals and lawyers about what they like and dislike about the current technology they use. Don’t be surprised if users have different (or even opposing) positions depending on how they use the software. One group may want a review platform that is scaled down without a lot of bells and whistles, while another group heavily relies on advanced analytics and artificial intelligence (AI) capabilities. This is common, especially among groups that handle vastly different matter types, and can actually be a valuable consideration during the evaluation process. For instance, in the scenario above, you know you will need to look for eDiscovery software that can flex and scale from the smallest matter to the largest, as well as one that can create different templates for disparate use cases. In this way, you can ensure you purchase one self-service, spectra eDiscovery software that will meet the diverse needs of all your users.Communicate with IT and data security teams to ensure that any platform conforms with their requirements.These two groups often end up being pulled into discussions too late once purchasing decisions have already been made. This is unfortunate, as they are integral to the implementation process, as well as to ensuring that all software is secure and meets all applicable data security requirements. Data security in eDiscovery is non-negotiable, so you want to be sure that the eDiscovery technology software you select meets your firm or organization’s data security requirements before you get too far along in the purchasing process.Create a prioritized list of the most important capabilities, functionality, and attributes to all the stakeholders once you’ve gathered feedback.Having a defined list of must-haves and desired capabilities will make it easier to vet potential technology software and ultimately help you identify a technology platform that fits the needs of all relevant stakeholders.ConclusionWith today’s advanced technology, attorneys and legal professionals should not have to deal with technology gaps in their self-service, spectra eDiscovery software, just as law firms and organizations should not have to blindly accept the higher eDiscovery cost and risk those gaps cause downstream. Powerful best-in-class technology for each step of the eDiscovery process is out there. Leveraging the steps above will help you find a self-service, spectra eDiscovery software solution that ties all the functionality you need under one seamless, easy-to-use interface.For more detailed advice about navigating the purchasing process for self-service, spectra eDiscovery software, download our self-service, spectra eDiscovery Buyer’s Guide here. ediscovery-review; ai-and-analyticsself-service, spectra, review, analytics, processing, blog, production, ediscovery-review, ai-and-analyticsself-service, spectra; review; analytics; processing; blog; productionsarah moran

Below is a copy of a featured article written by Kimberly Quan of Juniper Networks and John Del Piero of Lighthouse for Bloomberg Law.Whether it's Teams, Slack, Zendesk, GChat, ServiceNow, or similar solutions that have popped up in the market over the last few years, collaboration and workflow platforms have arrived. According to Bloomberg Law's 2020 Legal Technology Survey, collaboration tools are being used by 77% of in-house and 44% of law firm attorneys. These tools are even more widely used by workers outside of the legal field.With many companies planning to make remote working a permanent fixture, we can expect the existing collaboration tools to become even more entrenched and new competitors to arrive on the scene with similarly disruptive technologies.This will be a double-edged sword for compliance and in-house legal teams, who want to encourage technology that improves employee productivity, but are also wary of the potential information governance and eDiscovery risks arising because of these new data sources. This article explains the risks these tools can pose to organizations and provides a three-step approach to help mitigate those risks.Understand Litigation and Investigation RiskThe colloquial and informal nature of collaborative tools creates inherent risk to organizations, much like the move from formal memos to email did 20 years ago. Communications that once occurred orally in the office or over the phone are now written and tracked, logged, and potentially discoverable. However, a corporation's ability to retain, preserve, and collect these materials may be unknown or impossible, depending on the initial licensing structure the employee or the company has entered into or the fact that many new tools do not include features to support data retention, preservation, or collection.Government agencies and plaintiffs’ firms have an eye on these new applications and platforms and will ask specifically about how companies and even individual custodians use them during investigations and litigations. Rest assured that if a custodian indicates during an interview or deposition that she used the chat function in a tool like Teams or Slack, for example, to work on issues relevant to the litigation, opposing counsel will ask for those chat records in discovery. Organizations can mitigate the risk of falling down on their eDiscovery obligations because of the challenges posed bycollaboration tool data using this three-step approach:Designate personnel in information technology (IT) and legal departments to work together to vet platforms and providers.Develop clear policies that are regularly reviewed for necessary updates and communicated to the platform users.Ensure internal or external resources are in place to monitor the changes in the tools and manage associated retention, collection, and downstream eDiscovery issues.Each of these steps is outlined further below.Designate IT & Legal Personnel to Vet Platforms and Providers‍Workers, especially those in the tech industry, naturally want to be free to use whatever technology allows them to effectively collaborate on projects and quickly share information.However, many of these tools were not designed with legal or eDiscovery tasks in mind, and therefore can pose challenges around the retention, preservation and collection of the data they generate.Companies must carefully vet the business case for any new collaboration tool before it is deployed. This vetting process should entail much more than simply evaluating how well the tool or platform can facilitate communication and collaboration between workers. It also involves designating personnel from both legal and IT to work together to evaluate the eDiscovery and compliance risks a new tool may pose to an organization before it is deployed.The importance of having personnel from both legal and IT involved from the outset cannot be understated. These two teams have different sets of priorities and can evaluate eDiscovery risks from two different vantage points. Bringing them together to vet a new collaboration tool prior to deployment will help to ensure that all information governance and eDiscovery downstream effects are considered and that any risks taken are deliberate and understood by the organization in advance of deployment. This collaborative team can also ensure that preservation and discovery workflows are tested and in place before employees begin using the tool.Once established, this dedicated collaborative IT and legal team can continue to serve the organization by meeting regularly to stay abreast of any looming legal and compliance risks related to data generation. For example, this type of team can also evaluate the risks around planned organizational technology changes, such as cloud migrations, or develop workflows to deal with the ramifications of the near-constant stream of updates that roll out automatically for most cloud-based collaborative tools.Develop Clear Policies That Are Regularly Reviewed‍The number of collaborative platforms that exist in the market is ever evolving, and it is tempting for organizations to allow employees to use whatever tool makes their work the easiest. But, as shown above, allowing employees to use tools that have not been properly vetted can create substantial eDiscovery and compliance risks for the organization.Companies must develop clear policies around employee use of collaborative platforms in order to mitigate those risks. Organizations have different capabilities in restricting user access to these types of platforms. Historically, technology companies have embraced a culture where innovation is more important than limiting employees’ access to the latest technology. More regulated companies, like pharmaceuticals, financial services, and energy companies, have tended to create a more restrictive environment. One of the most successful approaches, no matter the environment or industry, is to establish policies that restrict implementation of new tools while still providing users an avenue to get a technology approved for corporate use after appropriate vetting.These policies should have clear language around the use of collaboration and messaging tools and should be frequently communicated to all employees. They should also be written using language that does not require updating every time anew tool or application is launched on the market. For instance, a policy that restricts the work-related use of a broad category of messaging tools, like ephemeral messaging applications, also known as self-destructing messaging applications, is more effective than a policy that restricts the use of a specific application, like Snapchat. The popularity of messaging tools can change every few months, quickly leading to outdated and ineffective policies if the right language is not used.Make sure employees not only understand the policy, but also understand why the policy is in place. Explain the security, compliance, and litigation-related risks certain types of applications pose to the organization and encourage employees to reach out with questions or before using a new type of technology.Further, as always with any policy, consider how to audit and police its compliance. Having a policy that isn't enforced issometimes worse than having no policy at all.Implement Resources to Manage Changes in Tools‍Most collaboration tools are cloud-based, meaning technology updates can roll out on a near-constant basis. Small updates and changes may roll out weekly, while large systemic updates may roll out less frequently but include hundreds of changes and updates. These changes may pose security, collection, and review challenges, and can leave legal teams unprepared to respond to preservation and production requests from government agencies or opposing counsel. In addition, this can make third-party tools on which companies currently rely for specific retention and collection methodologies obsolete overnight.For example, an update that changes the process for permissions and access to channels and chats on a collaborative platform like Teams may seem like a minor modification. However, if this type of update is rolled out without legal and IT team awareness, it may mean that employees who formerly didn't have access to a certain chat function may now be able to generate discoverable data without any mechanism for preservation or collection in place.The risks these updates pose mean that is imperative for organizations to have a framework in place to monitor and manage cloud-based updates and changes. How that framework looks will depend on the size of the organization and the expertise and resources it has on hand. Some organizations will have the resources to create a team solely dedicated to monitoring updates and evaluating the impact of those updates. Other organizations with limited internal access to the type of expertise required or those that cannot dedicate the resources required for this task may find that the best approach is to hire an external vendor that can perform this duty for the organization.When confronted with the need to collect, process, review, and produce data from collaboration tools due to an impending litigation or investigation, an organization may find it beneficial to partner with someone with the expertise to handle the challenges these types of tools present during those processes. Full-scale, cloud-based collaboration tools like Microsoft Teams and Slack are fantastic for workers because of their ability to combine almost every aspect of work into a single, integrated interface. Chat messaging, conference calling, calendar scheduling, and group document editing are all at your fingertips and interconnected within one application. However, this aspect is precisely why these tools can be difficult to collect, review, and produce from an eDiscovery perspective.With platforms like Teams, several underlying applications, such as chat, video calls, and calendars, are now tied together through a backend of databases and repositories. This makes a seemingly simple task like “produce by custodian” or “review a conversation thread” relatively difficult if you haven't prepared or are not equipped to do so. For example, in Teams communications such as chat or channel messages, when a user sends a file to another user, the document that is attached to the message is no longer the static, stand-alone file.Rather, it is a modern attachment, a link to the document that resides in the sender's OneDrive. This can beg questions as to which version was reviewed by whom and when it was reviewed. Careful consideration of versioning and all metadata and properties will be of the utmost importance during this process, and will require someone on board who understands the infrastructure and implications of those functions.The type of knowledge required to effectively handle collection and production of data generated by the specific tools an organization uses will be extremely important to the success of any litigation or investigation. Organizations can begin planning for success by proactively seeking out eDiscovery vendors and counsel that have experience and expertise handling the specific type of collaboration tools that the organization currently uses or is planning on deploying. Once selected, these external experts can be engaged early, prior to any litigation or investigation, to ensure that eDiscovery workflows are in place and tested long before any production deadlines.ConclusionCloud-based collaboration tools and platforms are here to stay. Their ability to allow employees to communicate and collaborate in real time while working in a remote environment is becoming increasingly important in today's world. However, these tools inherently present eDiscovery risks and challenges for which organizations must carefully prepare. This preparation includes properly vetting collaboration tools and platforms prior to deploying them, developing and enforcing clear internal policies around their use, monitoring all system updates and changes, and engaging eDiscovery experts early in the process.With proper planning, good collaboration between IT and legal teams and expert engagement, organizations can mitigate the eDiscovery risks posed by these tools while still allowing employees the ability to use the collaboration tools that enable them to achieve their best work.Reproduced with permission. Published March 2021. Copyright © 2021 The Bureau of National Affairs, Inc.800.372.1033. For further use, please contact permissions@bloombergindustry.com.chat-and-collaboration-data; ediscovery-review; microsoft-365emerging-data-sources, blog, corporate, chat-and-collaboration-data, ediscovery-review, microsoft-365,emerging-data-sources; blog; corporatebloomberg law

Legal tech was no match for 2020. Everyone’s least favorite year wreaked havoc on almost every aspect of the industry, from data privacy upheavals to a complete change in the way employees work and collaborate with data.With the shift to a remote work environment by most organizations in the early spring of 2020, we saw an acceleration of the already growing trend of cloud-based collaboration and video-conferencing tools in workplaces. This in turn, means we are seeing an increase in eDiscovery and compliance challenges related to data generated from those tools – challenges, for example, like collecting and preserving modern attachments and chats that generate from tools like Microsoft Teams, as well compliance challenges around regulating employee use of those types of tools.However, while collaboration tools can pose challenges for legal and compliance teams, the use of these types of tools certainly did help employees continue to work and communicate during the pandemic – perhaps even better, in some cases, than when everyone was working from traditional offices. Collaboration tools were extremely helpful, for example, in facilitating communication between legal and IT teams in a remote work environment, which proved increasingly important as the year went on. The irony here is that with all the data challenges these types of tools pose for legal and IT teams, they are increasingly necessary to keep those two departments working together at the same virtual table in a remote environment. With all these new sources and ways to transfer data, no recap of 2020 would be complete without mentioning the drastic changes to data privacy regulations that happened throughout the year. From the passing of new California data privacy laws to the invalidation of the EU-US privacy shield by the Court of Justice of the European Union (CJEU) this past summer, companies and law firms are grappling with an ever-increasing tangle of regional-specific data privacy laws that all come with their own set of severe monetary penalties if violated. How to Prepare for 2021The key-takeaway here, sadly, seems to be that 2020 problems won’t be going away in 2021. The industry is going to continue to rapidly evolve, and organizations will need to be prepared for that.Organizations will need to continue to stay on top of data privacy regulations, as well as understand how their own data (or their client’s data) is stored, transferred, used, and disposed of.Remote working isn’t going to disappear. In fact, most organizations appear to be heading to a “hybrid” model, where employees split time working from home, from the office, and from cafes or other locations. Organizations should prepare for the challenges that may pose within compliance and eDiscovery spaces.Remote working will bring about a change in employee recruiting within the legal tech industry, as employers realize they don’t have to focus talent searches within individual locations. Organizations should balance the flexibility of being able to expand their search for the best talent vs. their need to have employees in the same place at the same time.Prepare for an increase in litigation and a surge in eDiscovery workload as courts open back up and COVID-related litigation makes its way to discovery phases over the next few months.AI and advanced analytics will become increasingly important as data continues to explode. Watch for new advances that can make document review more manageable.With continuing proliferation of data, organizations should focus on their information governance programs to keep data (and costs) in check.To discuss this topic further, please feel free to reach out to me at SMoran@lighthouseglobal.com. ai-and-analytics; ediscovery-review; legal-operationscloud, analytics, emerging-data-sources, data-privacy, ai-big-data, blog, ai-and-analytics, ediscovery-review, legal-operations,cloud; analytics; emerging-data-sources; data-privacy; ai-big-data; blogsarah moran

We're excited to announce that season one of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution, is now live!We at Lighthouse recognize that podcasting has become a popular form of content consumption, and to support our mission of providing valuable information around the eDiscovery, compliance, and information governance space via our thought leadership efforts, we have decided to launch a podcast dedicated to just that.Law & Candor co-hosts, Bill Mariano and Rob Hellewell, alongside industry experts, explore the impacts and possibilities that new technology is creating for the space. Our dynamic duo, who have their finger on the pulse of the rapidly evolving world of legal technology, will discuss the latest trends and newsworthy topics that are dominating the eDiscovery revolution. Take a quick look at the episodes they showcase in season one:The Future is Now – AI and Analytics are Here to StayThe Truth Behind Data ReuseMicrosoft Office 365 Part 1: Microsoft’s Influence on the Next Evolution of eDiscoveryMicrosoft Office 365 Part 2: How to Leverage all the Tools in the Toolbox Moving to the Cloud Part 1: A Corporate JourneyMoving to the Cloud Part 2: A Law Firm JourneyThe episodes are short and easy to consume and each one shares key takeaways for you to take back to your team. Listen here or on a platform of your choice and follow us on Twitter for updates and to join in on the conversation.For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewmicrosoft, cloud, self-service, spectra, ai-big-data, data-re-use, blog, ediscovery-review,microsoft; cloud; self-service, spectra; ai-big-data; data-re-use; bloglighthouse

We are now past the midpoint of 2020, which means we are more than halfway through the first year of a brand new decade. This midway point is a great time to take a look at the hottest trends in the legal tech world and predict where those trends may lead us as we move further into the new decade.If we were evaluating future trends in legal tech during a normal year, there might be one or two uncertainties or prominent events from the first half of the year that we would need to take into account. Maybe a shift in global data safety laws or a change to the Federal Rules of Evidence. But, as I’m sure we’re all tired of reading, 2020 has not been a normal year (“the new normal”, “these uncertain times”, “these unprecedented events”, etc. etc. etc.). No matter how you phrase it, we can all agree that 2020 has been… unpredictable. Or to be a bit less understated: the first six months of 2020 have drastically changed how many corporations and law firms function on a day-to-day basis, and industry leaders are predicting that many of those changes will have a lasting effect. For example, a recent Gartner survey of company leaders from HR, legal and compliance, finance, and real estate industries showed that 82% of those responding plan to allow employees to continue working remotely in some capacity once employees are allowed back in the office, while close to half responded that they will allow employees to work remotely full time.So what does that mean for the legal tech industry? Well, while the world around us has changed dramatically due to the events of 2020, many of those changes actually dovetail quite nicely into where legal tech was already headed. In this article, we will look at the latest trends in legal tech and how 2020, in all its chaos, has affected those trends.SaaS self-service, spectra eDiscovery: The growing adoption of cloud services is leading us to a unique hybrid approach to managing eDiscovery programs: SaaS self-service, spectra eDiscovery solutions. This new subscription-based approach gives law firms and corporate legal teams the ability to take charge of their own fates by bringing their eDiscovery program in house, while leaving much of the security risks, costs, and IT burdens to a reputable, secure vendor that can house the data in a private cloud or within its own data centers. The benefit of controlling your own eDiscovery program in house are obvious. Legal teams would have the ability to control costs and access their data whenever and wherever they need to without the expense and hassle of having to go through a middle man. It would also give legal teams more control over their own costs, deadlines, and workflows, with the ability to fluidly scale up or down depending on case need. The self-service, spectra subscription approach is also unique in that it leaves the burden and risk of creating and managing an entire IT data storage infrastructure with the vendor. A security-minded vendor with SOC 2 and ISO 27001 security certifications can house data in a private cloud or their own data center, providing a completely secure environment without the overhead and risk of managing that data in house. A subscription service also may come with the reassurance that if a project or timeline becomes more burdensome than expected, the in-house team could easily pass off a workflow or entire project to the vendor seamlessly.In 2020, a SaaS self-service, spectra solution has the added benefit of being available in every location around the world, at any time. If a worldwide pandemic has taught us anything, it is that traveling to multiple locations throughout the world to set up data centers to handle the specific needs of a case or a client is no longer a feasible solution. Housing and accessing data in the Cloud does not require abiding by global travel restrictions or mandatory quarantines. A SaaS self-service, spectra model where data is stored in the Cloud allows for global expansion without concern for pandemics, natural disasters, or political uncertainty.Big Data Analytics: Big data analytics and technology assisted review (TAR) are certainly not new ideas to 2020. The technology and tools have existed for years and the legal industry has slowly been adopting them. (I say “slowly” in contrast to how fast these tools are developed and adopted in other areas outside of the legal field.) The need to find reliable ways to comb through massive amounts of data in the eDiscovery and compliance arenas will only grow, and we can expect that the technology will only continue to improve and become even more reliable.One could argue that the biggest hindrance to big data analytics in the legal world is not the advancement of the technology, but rather the ability and willingness of many lawyers and courts to adopt that technology as a defensible, necessary legal tool in the modern world of big data. The legal field is notoriously slow to adopt new technology. As a personal example, I clerked for a prominent, incredibly smart criminal defense attorney who still used carbon paper to make copies of important court filings. This occurred during the same year that the third season of Lost aired (or the same year that the first season of Madmen premiered - pick your reference. Either way, not that long ago). And every law firm is rife with stories of the old-school partner who holes up in the firm library (the existence of which could also be an example to my point, in and of itself) because she doesn’t believe in online legal research. While the practice of law is steeped in an awe-inspiring mix of tradition and history, it can also be frustratingly slow to expand on that tradition because it refuses to use a copier. Even Don Draper had a copier by the second season.However, if we can say one positive thing about 2020, it is that the last six months have pushed the legal world into the technological future more than any other time period to date. Almost every in-house counsel, law firm, and court across the globe has been forced to find a way to conduct its business in a completely remote environment. This means that judges, law firms, and in-house counsel are facing the reality that the legal world needs to rely on and adapt to technology in order to survive. One hopes that this new reality helps lead to a more robust adoption of technological advancement in the legal world in general, and hopefully, a shift away from the reactionary relationship the legal industry always seems to have with technology. Because data volumes will only continue to explode and there will come a time in the near future when it will not be defensible to tell a judge or a client that discovery may take years in order to allow time for a team of 200 contract attorneys to look at each individual document that hits on a search term. Analytics will eventually be a requirement for a defensible eDiscovery program, and 2020 may be the year that helps many in the legal field take a more proactive approach to its adoption.New sources of data (i.e. collaboration tools): Like big data analytics, online collaboration tools like Teams and Slack are not new to 2020, but this year has certainly helped push the use of these tools to the forefront of many companies’ day-to-day business. It seems like new collaboration tools arise every month and companies are increasingly pushing employees to utilize them. Organizations are realizing the value of these collaboration tools in a post-COVID environment, where online collaboration is not only preferable, but absolutely critical. Not to repeat some of 2020’s greatest memes, but I’m sure we’ve all seen the 2020 adage that this is the year that we all realized that not only could that meeting have been an email, that email could have been an instant message. Data actually proves that theory to be true. Microsoft for example, found that chat messages within Microsoft Teams meetings increased over 10x from March 1 to June 1.The widespread use of these types of tools, in turn, generates more and more unique data that needs to be accounted for during an eDiscovery or compliance event Going forward, organizations will need to ensure that they know which tools their employees or contractors are using, what data those tools generate, and how to defensibly collect, process, and review that data in the event of a lawsuit or investigation (or retain a vendor who can guide them through that process). Which brings us to our final 2020 trend…Continuous program update subscription services: Going hand-in-hand with the above, watch out for eDiscovery programs and solutions that can manage the continuous delivery of program updates on all of the applications and platforms that organizations use to effectively perform their work. Gone are the days when the same data collection or processing workflow could be used for years at a time and still be defensible. From iPhone iOS to Teams, systemic updates to work applications and platforms can now roll out on an almost weekly basis, and it is imperative that legal and compliance teams stay on top of those updates and adapt to them in order to ensure that company information remains secure and that any data generated can be defensibly collected and processed when needed. In 2020 and beyond, look for technologically advanced eDiscovery subscription services that give companies the ability to prepare for and stay ahead of the never-ending stream of software updates.To discuss this topic further, please feel free to reach out to me at SMoran@lighthouseglobal.com.ai-and-analytics; ediscovery-review; legal-operationscloud, ai-big-data, blog, ai-and-analytics, ediscovery-review, legal-operations,cloud; ai-big-data; blogsarah moran

This year’s Legalweek conference was back to an in-person event in New York City — a significant change from the virtual format in 2021. Folks who hadn’t seen each other in person in over two years (or met for the first time in person) were able to talk and exchange ideas while sharing a hug, a meal, or a drink. Over and over again, the words, “It’s so good to see you, in person!” echoed throughout hallways and conference rooms. But as good as it feels to reconnect, it was also abundantly clear that the pandemic has fundamentally and permanently altered our world. There is no return to the “normal” we knew prior to March of 2020. The pandemic has changed us. Over the last two years, we have reprioritized what’s important in our lives, which has changed not only where we work, but how we work. And technology, as it always does, has evolved to keep up with those changes. As we emerge into this new world, our eyes blinking in the sun, these changes may fill us with anxiety. Change, after all, can be scary. But as Don Draper, the fictional Madmen character, once said when talking to a client about cultural change in 1960s New York City: “Change is neither good nor bad, it simply ‘is.’ It can be greeted with terror or joy — a tantrum that says, ‘I want it the way it was,’ or a dance that says, ‘Look, something new!’" Below, I’ve outlined some key industry changes that were discussed throughout Legalweek, as well as how legal technology companies can help law firms and organizations greet these changes as an opportunity, rather than something to be feared. The virtual workforce revolution is here to stay The massive and abrupt pivot to remote working for organizations and law firms is not a blip that will reverse itself once the pandemic “ends.” Prior to 2020, it was a trend bubbling under the surface. The pandemic simply accelerated that trend more quickly than previously anticipated, and in doing so, permanently changed the landscape of white-collar careers. Most young adults who entered the workforce over the last two years have never known a world where work had to take place solely in an office setting. Meanwhile, more experienced workers—suddenly able to reap the flexibility that remote working provides—also do not seem keen to go back to a more rigid office-based work environment. And the younger generations waiting in the wings to enter the workforce over the next five to ten years have grown up learning and socializing in much more immersive virtual settings than any previous generation. As they become consumers and employees, technology will continue to evolve to accommodate their comfort interacting in those virtual environments. With a worldwide workforce shortage that does not seem like it well ebb anytime soon, this modern workforce will have the upper hand when it comes to demanding a more flexible, remote work environment, as well as access to the technology that facilitates it. Thus, organizations will not only have to adapt to these changes—they may need to lean heavily into them to survive. We can see the harbingers of this sea change even today. More and more companies are entering the metaverse , investing in NFTs, and utilizing virtual reality (VR) technology to perform work that would have typically been done in person or on flat screens (like training new employees). Microsoft, developers of one of the world’s most heavily used cloud collaboration and work platforms (M365 and Teams), also announced plans to introduce VR technology in 2022 that will work in conjunction with their existing technology, facilitating a more immersive virtual remote working experience for workers around the world. All these potential new data sources will significantly increase challenges from a data governance, data privacy, and eDiscovery perspective. But rest assured, the work that legal technology providers are doing now to put better systems in place to handle existing cloud-based tools will help lay the framework for how we handle data from the metaverse and other new sources in the future. For example, some eDiscovery providers and lawyers are already advocating for a move away from the traditional eDiscovery “custodial” ownership framework in order to accommodate how cloud-based data is stored and interacted with in organizations. Forward-thinking eDiscovery service providers are also advocating for a more holistic view of eDiscovery, one that begins at the data source and spans the entire data lifecycle—which will be a necessity as we move into a more virtual-based workplace. Technology providers are also starting to factor eDiscovery, data privacy, and compliance issues into future roadmaps and upgrades—making it easier to manage, search, and export data from new data sources for eDiscovery and compliance purposes. There is no magic bullet—a risk balancing act The shift to a more virtual world significantly increases risk for organizations and the law firms that represent them. Utilizing cloud-based tools and newer technology to facilitate a more virtual workplace will be increasingly important for organizations. However, due to the volume of data, and the speed at which it’s created, organizations will have to accept increased risks related to data privacy, data security, compliance, eDiscovery, etc. In effect, in today’s cloud-based world, there is no magic bullet that will completely eliminate risk caused by the proliferation and speed of data. Organizations are learning to balance risk and innovation when it comes to technology, rather than take an “all or nothing” approach. To do so, stakeholders from across the company must have a seat at the table when deciding how much risk they’re willing to take on in order to keep their employees productive and customers satisfied via technology. Knowledgeable legal technology service providers are already helping organizations adapt to this balancing act. Companies that have dedicated cloud technology experts can help their clients understand the technology they are using and how it works within their own environment. They can also help their clients staying abreast of ever-evolving risks presented by cloud-based technology and provide risk mitigation strategies that fit within the priorities of the organization. An increasing need to lean on managed service providers Today’s cloud-based tools and applications are increasingly complicated and present increased risks that must be managed. Additionally, due to global workforce shortages (i.e., “the great resignation) and unpredictable economic conditions (caused not only by the pandemic but by market uncertainty around Russia’s invasion of Ukraine, increasing gas prices, supply shortages, inflation, etc.), employees are often being asked to do more work with less budget and resources. Together, these two factors have led organizations and law firms to lean more on outsourcing specific segments and technology processes to outside service providers. The benefits of partnering with a trustworthy service provider to manage segments of the organization that require specialized expertise are manifold. The right service provider will have experts on staff who are wholly dedicated to understanding and managing specific technology, processes, and risk. Offloading management to those partners allows organizations to refocus on their own underlying mission. Service providers may also be better positioned to advocate for a company’s needs with pure technology providers because they have an existing partnership with those companies. This can help organizations fill technology gaps without spending weeks or months trying to negotiate with technology providers. Partnering with service providers also allows the organization to offload risks associated with the management of specific technology or processes to a company that is much better equipped to understand and take on that risk. Outsourcing work to a service provider can also significantly lower overhead costs and allow organizations to stay leaner and nimbler — empowering them to focus on tasks that add value to the underlying business while providing relief to overworked employees. In short, a good legal technology service partner can become an extension of an organization’s own team while lowering overhead and risk. Diversity can no longer be just a numbers game Over the last few years, we saw organizations and law firms focusing more on diversity efforts. Often, this focus was merely numerical, intended to increase the headcount of diverse staff. While this effort is well-intended (and long overdue), we are now seeing more demand for a deeper commitment to diversity and inclusion that goes beyond statistics, diversity training, and simple corporate statements. Today’s workforce, spurred on in part by a new generation of employees, are demanding that organizations be truly committed to diversity and equality on a deeper level—with action that is evident across the organization, from leadership profiles, to internal and external teams, to opportunities for advancement, to vendor selection, etc. And due to labor shortages, this new workforce has the power to effect change by refusing to work for companies that can’t demonstrate this type of commitment. Both the legal and technology industries have historically suffered from a lack of diversity. This is evident from the diversity gaps we still see in the industry today. However, this lack of diversity also presents an opportunity for legal technology companies to make a more significant impact. There is no downside to leaning into diversity. In fact, studies have shown that diverse companies are more successful. Legal technology companies have an opportunity to lead the way by putting dedicated systems in place to ensure that their leadership is diverse, that diversity is represented across all teams and company segments, that annual review processes and career advancement within the company are focused on equality, and that employees from underrepresented communities feel supported and seen within the company. Legal technology companies also have a unique opportunity to support groups that are dedicated to increasing legal and technology education and training opportunities for underrepresented communities (which is often at the root of the diversity problem across both industries). In this way, legal technology companies can help lead by example for the organizations and law firms they serve — showing that truly, a more diverse company is a more innovative company. Conclusion The world we are facing in 2022 is much different than the pre-pandemic world we left behind. The changes we are encountering today can present significant challenges to organizations and law firms — but they also present unique opportunities for growth. Legal technology companies can help both segments take advantage of these opportunities and emerge into a brighter future. ediscovery-reviewmanaged-services, cloud-migration, cloud-services, blog, ediscovery-review,managed-services; cloud-migration; cloud-services; blogsarah moran

Recently, I had the opportunity to (virtually) attend the first three days of Legalweek, the premier conference for those in the legal tech industry. Obviously, this year’s event looked much different than past years, both in structure and in content. But as I listened to legal and technology experts talk about the current state of the industry, I was happily surprised that the message conveyed was not one of doom and gloom, as you might expect to hear during a pandemic year. Instead, a more inspiring theme has emerged for our industry - one of hope through innovation.Just as we, as individuals, have learned hard lessons during this unprecedented year and are now looking towards a brighter spring, the legal industry has learned valuable lessons about how to leverage technology and harness innovation to overcome the challenges this year has brought. From working remotely in scenarios that previously would have never seemed possible, to recognizing the vital role diversity plays in the future of our industry – this year has forced legal professionals to adapt quickly, utilize new technology, and listen more to some of our most innovative leaders.Below, I have highlighted the key takeaways from the first three days of Legalweek, as well as how to leverage the lessons learned throughout this year to bring about a brighter future for your organization or law firm.“Human + Machine” not “Human vs. Machine” Almost as soon as artificial intelligence (AI) technology started playing a role within the legal industry, people began debating whether machines could (or should) eventually replace lawyers. This debate often devolves into a simple “which is better: humans or machines” argument. However, if the last year has taught us anything, it is that the answers to social debates often require nuance and introspection, rather than a “hot take.” The truth is that AI can no longer be viewed as some futuristic option that is only utilized in certain types of eDiscovery matters; nor should it be fearfully viewed as having the potential to replace lawyers in some dystopian future. Rather, AI has become essential to the work of attorneys and ultimately will be necessary to help lawyers serve their clients effectively and efficiently.1Data volumes are exponentially growing year after year, so much so that soon, even the smallest internal investigation will involve too much data to be effectively reviewed by human eyes alone. AI and analytics tools are now necessary to prioritize, cull, and categorize data in most litigations for attorneys to efficiently find and review the information they need. Moreover, advancements in AI technology now enable attorneys to quickly identify categories of information that previously required expensive linear review (for example, leveraging AI to identify privilege, protected health information (PHI), or trade secret data).Aside from finding the needle in the haystack (or simply reducing the haystack), these tools can also help attorneys make better, more strategic counseling and business decisions. For example, AI can now be utilized to understand an organization’s entire legal portfolio better, which in turn, allows attorneys to make better scoping and burden arguments as well as craft more informed litigation and compliance strategies.Thus, the age-old debate of which is better (human or machine learning) is actually an outdated one. Instead, the future of the legal industry is one where attorneys and legal professionals harness advanced technology to serve their clients proficiently and effectively.Remote Working and Cloud-Based Tools Are Here to StayOf course, one of the biggest lessons the legal industry learned over the past year is how to effectively work remotely. Almost every organization and law firm across the world was forced to quickly pivot to a more remote workforce – and most have done so successfully, albeit while facing a host of new data challenges related to the move. However, as we approach the second year of the pandemic, it has become clear that many of these changes will not be temporary. In fact, the pandemic appears to have just been an accelerator for trends that were already underway prior to 2020. For example, many organizations were already taking steps to move to a more cloud-based data architecture. The pandemic just forced that transition to happen over a much shorter time frame to facilitate the move to a remote workforce.This means that organizations and law firms must utilize the lessons learned over the last year to remain successful in the future, as well as to overcome the new challenges raised by a more remote, cloud-based work environment. For example, many organizations implemented cloud-based collaboration tools like Zoom, Slack, Microsoft Teams, and Google Workspace to help employees collaborate remotely. However, legal and IT professionals quickly learned that while these types of tools are great for collaboration, many of them are not built with data security, information governance, or legal discovery in mind. The data generated by these tools is much different than traditional e-mail – both in content and in structure. For example, audible conversations that used to happen around the water cooler or in an impromptu in-person meeting are now happening over Zoom or Microsoft Teams, and thus may be potentially discoverable during an investigation or legal dispute. Moreover, the data that is generated by these tools is structured significantly differently than data coming from traditional e-mail (think of chat data, video data, and the dynamic “attachments” created by Teams). Thus, organizations must learn to put rules in place to help govern and manage these data sources from a compliance, data security, and legal perspective, while law firms must continue to learn how to collect, review, and produce this new type of data.It will also be of growing importance in the future to have legal and IT stakeholder collaboration within organizations, so that new tools can be properly vetted and data workflows can be put in place early. Additionally, organizations will need a plan in place to stay ahead of technology changes, especially if moving to a cloud-based environment where updates and changes can roll out weekly. Attorneys should also consider technology training to stay up-to-date and educated on the various technology platforms and tools their company or client uses, so that they may continue to provide effective representation.Information Governance is Essential to a Healthy Data StrategyRelated to the above, another key theme that emerged over the last year is that good information governance is now essential to a healthy company, and that it is equally important for attorneys representing organizations to understand how data is managed within that organization.The explosion of data volumes and sources, as well as the unlimited data storage capacity of the Cloud means that it is essential to have a strong and dynamic information governance strategy in place. In-house counsel should ensure that they know how to manage and protect their company’s data, including understanding what data is being created, where that data resides, and how to preserve and collect that data when required. This is important not only from an eDiscovery and compliance perspective but also from a data security and privacy perspective. As more jurisdictions across the world enact competing data privacy legislation, it is imperative for organizations to understand what personal data they may be storing and processing, as well as how to collect it and effectively purge it in the event of a request by a data subject.Also, as noted above, the burden to understand an organization’s data storage and preservation strategy does not fall solely on in-house counsel. Outside counsel must also ensure they understand their client’s organizational data to make effective burden, scoping, and strategy decisions during litigation.A Diverse Organization is a Stronger OrganizationFinally, another key theme that has emerged is around recognizing the increasing significance that diversity plays within the legal industry. This year has reinforced the importance of representation and diversity across every industry, as well as provided increased opportunities for education about how diversity within a workforce leads to a stronger, more innovative company. Organizational leaders are increasingly vocalizing the key role diversity plays when seeking services from law firms and legal technology providers. Specifically, many companies have implemented internal diversity initiatives like women leadership programs and employee-led diversity groups and are actively seeking out law firms and service providers that provide similar opportunities to their own employees. The key takeaway here is that organizations and law firms should continue to look for ways to weave diverse representation into the fabric of their businesses.ConclusionWhile this year was plagued by unprecedented challenges and obstacles, the lessons we learned about technology and innovation over the year will help organizations and law firms survive and thrive in the future.To discuss any of these topics more, please feel free to reach out to me at SMoran@lighthouseglobal.com.1 In fact, attorneys already have an ethical duty (imposed by the Rules of Professional Conduct) to understand and utilize existing technology in order to competently represent their clients.ai-and-analytics; ediscovery-review; legal-operationscloud, information-governance, ai-big-data, blog, ai-and-analytics, ediscovery-review, legal-operations,cloud; information-governance; ai-big-data; blogsarah moran

The goal of Practice Direction 57AD (PD57AD, previously known as the Disclosure Pilot Scheme) is to modernise the UK’s disclosure practice. This transformation is essential because the traditional, manual, and combative approach to disclosure is unsustainable in the face of today’s massive data volumes and ever-evolving data sources. Manually collecting and reviewing millions of documents one-by-one has become prohibitively expensive, impossibly time consuming, and prone to the risk of both under and over disclosure. When you add in the combative approach between opposing parties, the traditional disclosure process becomes a recipe for skyrocketing legal costs, missed deadlines, and data issues that can derail entire matters. Conversely, a more cooperative approach that leverages AI technology can help improve the process—by allowing attorneys to focus their expertise on critical parts of the matter and refining AI tools to better handle data now and for future, related matters.Thus, PD57AD focuses on two pivotal elements to modernise disclosure: cooperation and technology. Specifically, PD57AD requires parties to “liaise and cooperate with the legal representatives of the other parties to the proceedings…so as to promote the reliable, efficient, and cost-effective conduct of disclosure, including through the use of technology.” Similarly, the Disclosure Review Document asks that each party outline how they “intend to use technology assisted review/data analytics to conduct a proportionate review of the data set” and further reminds parties of their duty to cooperate. Through PD57AD, legal teams’ relationships to each other and with technology is changing in a few crucial ways that present opportunities to work smarter, more cost effectively , and with greater agility.The duty to cooperateJudges are increasingly focusing on the language requiring cooperation between parties in PD57AD and will admonish counsel who attempt to use the disclosure process as a tool to punish an opposing party. For instance, in McParland & Partners Ltd v Whitehead, when a dispute arose involving the framing of the issues of disclosure, the judge took the opportunity to broadly remind both parties of the following: “It is clear that some parties to litigation in all areas of the Business and Property Courts have sought to use the Disclosure Pilot as a stick with which to beat their opponents. Such conduct is entirely unacceptable, and parties can expect to be met with immediately payable adverse costs orders if that is what has happened.”As data volumes grow and PD57AD becomes more cemented into the fabric of UK’s disclosure practice, there is a growing intolerance for “weaponised” disclosure practices by courts. Certainly, parties can expect that the days of “data dumping” (i.e., the strategy of over collecting and producing documents to bury the opposing party in data) or conversely, winning burden arguments related to the cost and time of manual review, are over. The duty to leverage technology Instead of this combative approach, courts will expect that parties come together cooperatively to agree on the use of technology to perform targeted disclosure that is both more cost effective and efficient. Indeed, in a cloud-based world, this symbiotic relationship between technology and legal is the only successful path forward for an effective disclosure process. Under this modern approach, the technology used to collect, cull, review, and produce data must be leveraged in such a way that results can be verified by opposing counsel and judges. This means that all workflows and processes must be transparent, defensible, and agreed upon by opposing counsel. Even prior to the implementation of the Disclosure Pilot Scheme in 2018, judges had begun to crack down on parties who attempted to “go it alone” by unilaterally leveraging technology to cull or search data in a non-transparent way, without the consent of opposing counsel and/or without implementing industry standard best practices. For example, in Triumph Controls UK Ltd., the judge explicitly admonished a party for deploying a computer assisted review (CAR) search strategy overseen by “ten paralegals and four associates” rather than a “single, senior lawyer who has mastered the issues in the case” to ensure that the criteria for relevance was consistently applied to effectively teach the CAR technology. He also rebuked the party’s CAR approach because it was not transparent and could not be independently verified. Because these technology best practices were not followed, the judge forced the producing party to go back and cooperatively agree with opposing counsel on an alternative review methodology to sample and re-review a portion of the original dataset. The future of disclosure for counsel and clients The modernisation of the disclosure process through cooperation and technology means that it will be increasingly imperative that each party has the requisite legal and technology expertise to meet the requirements of PD57AD. Specifically, each party must have a barrister who understands disclosure law and can guide them through each step of the process in a way that complies with PD57AD. Each party should also have an expert who understands how to implement technology to perform targeted, efficient, and transparent disclosure workflows. As seen from legal decisions emanating around PD57AD, parties without this expertise who attempt to “wing it” will increasingly find themselves facing delayed proceedings, hefty legal costs, and unfavourable judgements by courts. Law firms or corporations that don’t have the requisite expertise internally must look for an external partner that does. This is where an experienced managed review partner can provide a true advantage to both law firms and their clients. Parties should look for a partner who can provide a team of technology experts and experienced barristers, working in tandem and leveraging the industry’s best technology. This team should be ready to jump in at the outset of every matter to understand the nuances of the client’s data, as well as the underlying legal issues at play, so that each step of the disclosure process is performed transparently, defensibly, and efficiently. Over time, a managed review team can become a valuable extension of corporate in-house and law firm teams. This partner can use institutional knowledge, gained by working with the same clients across multiple matters, to create customised, strategic, and automated disclosure workflows. These tailored processes, designed directly for a client’s data infrastructure and technology, can save millions and achieve better outcomes. In turn, law firms can refocus their attention on the evidence that actually matters, while assuring their clients that the disclosure process is contributing to lower legal costs and better overall results.ConclusionUnder the modern approach to disclosure, parties must have someone on their team with the necessary legal and technology expertise to perform the type of targeted, cooperative, and transparent disclosure methodology now required by PD57AD. This partnership between legal and technology is truly the only path forward for a successful disclosure endeavour in the face of today’s more voluminous and complicated datasets. Parties that do not have this expertise should look for an experienced managed review partner who can provide a consistent team of legal and technology experts who can perform each step of the disclosure process efficiently, transparently, and defensibly. ai-and-analytics; ediscovery-reviewreview, ai-big-data, blog, ai-and-analytics, ediscovery-reviewreview; ai-big-data; blogjennifer cowman

Co-authored by Hannah Fotsch, Associate, Lathrop GPM; Samuel Butler, Associate, Lathrop GPM; and Casey Van Veen, Vice President Global eDiscovery Solutions, Lighthouse2020 has been an incredibly tough year for many businesses, with companies big and small shuttering at a record pace due to COVID-19 restrictions and significant reductions in customer travel and spending. But there is one surprising business type that many people seem to want to continue to invest in despite the pandemic: the franchise business model.For example, both the U.S. Chamber of Commerce and Business.com recently highlighted franchise-model businesses that were not only surviving the pandemic and associated lockdowns, but thriving. And in fact, one of those thriving franchise business types called out by the authors was franchise consulting businesses (consultants that help match aspiring franchise owners with franchise opportunities). Apparently, the pandemic has actually increased investment interest in franchise opportunities.There may be a few different reasons why people are looking to the franchise business model during an economic downturn. Many franchise businesses have the benefit of a widely known name brand and market presence. Many have the benefit of leveraging a fully baked business model ‚Äì one that has presumably already been proven successful. Many also have more support than solo businesses in a variety of key business development areas, including marketing, advertising, and training. In short, the franchise business model may have more appeal during this economic upheaval than a solo business model because people trust the support it can provide in times of economic trouble.However, there are still several common pitfalls that can drag profits down and slow economic growth, leaving the franchise model just as exposed to failure as a solo business model in this time of economic uncertainty. One of those pitfalls is litigation and internal investigations, and the resulting eDiscovery challenges those two can raise. Not only do businesses operating within a franchise model face the same types of litigation and employee workplace issues that all other businesses face ‚Äì they may also have to deal with added litigation that is unique to the franchisor-franchisee relationship. All of this means increased cost and overhead, especially when it comes to preserving, collecting, reviewing, and producing the required data during the discovery phase.In this article, we discuss the legal eDiscovery challenges and the primary legal issues that we see affecting franchise businesses, large and small. We‚Äôll also provide best-practice tips that can help keep eDiscovery costs down and enable franchise businesses to utilize their advantage and continue to survive and thrive during this trying time.Legal eDiscovery ChallengesThere are four main challenges we see affecting franchise businesses currently: (1) the explosion of data sources; (2) the increased frequency of internal investigations and compliance matters; (3) the lack of a playbook to ensure discovery is managed in a low risk, low-cost manner; and (4) big data challenges.Explosion of Data SourcesWalk through any franchise store, restaurant, or facility today and you will be amazed at the number of devices and systems that must be contemplated in discovery.Fixed systems on property: Video security, card key access, time clock, email, and desktop computersCloud-based systems: Many of the above systems can also be found in the Cloud along with M365 and Google Suite of business documents, email, collaboration tools, and backupsEmployee sources: Personal email, cell phones (video, app chat, texts), iPads, and tabletsCorporate maintained systems: Marketing documents, HR systems, Material Safety Data Sheets (MSDSs), proprietary training, and competitive analysis documentationMoreover, employees at different franchise businesses may often choose to communicate on different platforms, which can exponentially diversify data sources. This amount and variety of sources can pose a myriad of challenges from an eDiscovery perspective.The duty to preserve data begins as soon as litigation is ‚Äúreasonably foreseeable.‚Äù Thus, once an allegation that may lead to litigation surfaces, the clock begins ticking, not only to effectively respond to the allegation but also to ensure that evidentiary data at issue is preserved. And once discovery begins, that preserved data will need to be collected. All of this can present challenges for the ill-prepared: How do you collect data from employees‚Äô personal devices? What are the local state and federal rules regarding the privacy of personal devices? How does collecting the data differ from Apple device vs Android devices? The need to be aware of platforms that create data and the possibilities for collecting that data from them must be addressed before litigation begins, or businesses risk losing data that could be essential to litigation.Key takeaway: Know your data sources as a standard course of business. Make sure that you know where data resides, how it can be accessed, and what can and cannot be collected from data sources.Internal Investigations & Compliance MattersThere has been a drastic increase in internal investigations and compliance matters with franchise clients recently. Hotline and compliance phone line tips, allegations around employee theft, and suspected fraud are on the rise. The key to resolving these types of investigations quickly and cost efficiently is speed. Attorneys and company executives need to know as soon as possible: is there truly an issue, how far does it go, how long has it been happening, how many employees does this effect, and what is the exposure (financially, socially). It is important to develop workflows and tools to help decision-makers and their legal experts sift through the mountains of data quickly.To understand the importance of this, consider this example. A company sales representative leaves the business and does not disclose their next line of work. A tip line reveals they the representative may have left for a competitor. Shortly thereafter, business deals that were executed and even ones in the pipeline suddenly disappear to a competitor. The former employer quickly conducts a forensic investigation on the representative‚Äôs laptop computer. Despite their attempt to hide their activity, the investigation reveals that the representative had downloaded proprietary customer lists, price sheets, and other valuable IP during their last week of employment and had also moved large chunks of confidential information from the company‚Äôs servers to thumb drives and utilized their personal email to store work communications. Without a strategic plan in place laying out how to quickly execute a forensic internal investigation in this type of situation, the company would have lost substantial revenue to a competitor.Companies that are particularly concerned about former employees stealing proprietary information can even go further than creating an effective investigatory and remediation strategy ‚Äì putting a departing employee forensic monitoring program in place can prevent this time of abuse from happening in the first place.Key takeaway: Have a program in place to certify that departing employees leave with only their personal belongings and not proprietary company information.Lack of an eDiscovery PlaybookPlaybooks come in many forms today: user manuals, company directives, cooking instructions, and recipe guides. A successful playbook for the legal department will establish a practical process to follow should a legal or compliance issue arise. Playbooks, like a checklist for a pilot about to fly a plane, ensure that everyone is following a solid process to avoid risk. These documents also prevent rogue players from recreating the wheel and going down potentially expensive rabbit holes.Repetitive litigation situations are particularly well suited for acting according to playbooks, and standardizing the response to these situations helps to ensure the predictability of both outcomes and expenses. For example, these documents can be as granular as necessary but typically include a few key topics such as:The process for responding to a 3rd party subpoena, service, or allegation of wrongdoingThe company‚Äôs systems that are typically subject to discoveryIT contacts that can help gather the information/dataA list of service providers/trusted partners to assistStandard data processing and production specifications (i.e. time zone, global deduplication, single-page TIFF images 400 dpi, text, and metadata fields)Preferred technologies to search, review, and produce documents (i.e. Relativity)Key takeaway: Playbooks can shave days off of the engagement process with outside counsel and data management companies. Having a repeatable process and plan on day one will save time and money as well as reduce risk.Big Data ChallengesFranchisors face issues in litigation that are unique to the industry, from vicarious liability claims involving the actions of franchisees or their employees to the sheer unpredictability that comes from extensive business relationships involving franchisees of a breathtaking range of sophistication. An increase in litigation leads to an increase in data. Even a run-of-the-mill dispute can lead to the need to gather (and potentially review) more than 100,000 documents. Add one or two more small disputes, and the amount of data quickly becomes unmanageable (and expensive).Fortunately, there have been impressive advances in the field of advanced legal analytical and artificial intelligence (AI). These innovative eDiscovery tools can help legal professionals analyze data to quickly identify documents that are not important to the litigation or investigation (thereby eliminating the need to review them), as well find the ‚Äústory‚Äù within a data set. For example, some analytical tools can help identify code words that an employee might have used to cover up nefarious actions, or analyze communications patterns that allow attorneys to identify the bad actors in a given situation. Other tools now have the capability of analyzing all of the company‚Äôs previously collected and attorney-reviewed data, which substantially reduces the need for attorney review in the current matter.All of these tools work to reduce data burden, which in turn reduces costs and increases efficiency.Key takeaway: Take the time to learn what eDiscovery solutions are available on the market today and how you can leverage them before you are faced with a need to use them.To discuss this topic more, please feel free to reach out to me at CVanVeen@lighthouseglobal.com. ediscovery-review; ai-and-analyticscloud, ai-big-data, compliance-and-investigations, ediscovery-process, blog, ediscovery-review, ai-and-analyticscloud; ai-big-data; compliance-and-investigations; ediscovery-process; blogcasey van veen

The Law & Candor podcast is back for Season 8, continuing its exploration of the legal technology revolution. Our co-hosts return with a stellar slate of expert guests and captivating conversations, all striving to elevate the current state of our industry and look to the future.Bill Mariano and Rob Hellewell are back to help lead those discussions in six easily digestible episodes that cover a range of topics, including: AI and linguistics in eDiscovery, staying ahead of AI innovation, family versus four corner review, cross-matter review strategy and implementation, unindexed items in Microsoft 365, and the rise of wearable devices and health-related apps.Episode 1. Finding Lingua Franca: The Power of AI and Linguistics for Legal TechnologyEpisode 2. Staying Ahead of the AI CurveEpisode 3. eDiscovery Review: Family Vs. Four CornerEpisode 4. Achieving Cross-Matter Review Discipline, Cost Control, and EfficiencyEpisode 5. Understanding Microsoft 365 Unindexed Items Episode 6. Getting Personal—Wearable Devices, Data, and CoGetting Personal—Wearable Devices, Data, and Compliance Listen now or bookmark individual episodes to listen to them later, and be sure to follow the latest updates on Law & Candor's Twitter. And if you want to catch up on past seasons or special editions, click here.For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewblog, podcast, ediscovery-review,blog; podcastlighthouse

With a new look, and new co-host, Law & Candor returns for its 10th season. Paige Hunt, Vice President of Global Discovery Solutions at Lighthouse, joins Bill Mariano for more compelling conversations with industry leaders and luminaries in the legal and technology spaces.In six brand new episodes, our guests and co-hosts explore some of the most pressing issues for the industry, including: data governance in the work-from-home era; improving mental health in legal and eDiscovery; the power of review analytics; championing diversity, equity, and inclusion; the role of AI in cross-border data transfer; and self-service, spectra solutions for internal investigations.Listen and learn more about the episodes : Episode 1: Data Governance for the BYOD AgeEpisode 2: Review Analytics for a New EraEpisode 3: Legal’s Mental Health ImperativeEpisode 4: Anonymization and AI: Critical Technologies for Moving eDiscovery Data Across Borders Episode 5: Investigative Power: Utilizing Self Service Solutions for Internal Investigations  Episode 6: A Journey from One to All in Legal with Diversity, Equity, and Inclusion   For more news and updates, follow Law & Candor on Twitter. And if you want to catch up on past seasons or special editions, click here.For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com. ediscovery-reviewblog, podcast, ediscovery-review,blog; podcastmitch montoya

This eDiscovery Day, a day dedicated to educating industry professionals around growing trends and current challenges, we are excited to bring you season six of Law & Candor, the podcast wholly devoted to pursuing the legal technology revolution.Co-hosts, Bill Mariano and Rob Hellewell, are back for another riveting season of Law & Candor with six easily digestible episodes that cover a range of hot topics such as how cellular 5G increases fraud and misconduct risk to tackling modern attachment challenges in G-Suite, Slack, and Teams. This dynamic duo, alongside industry experts, discuss the latest topics and trends within the eDiscovery, compliance, and information governance space as well as share key tips for you and your team to take away. Check out season six's lineup below:Does Cellular 5G Equal 5x the Fraud and Misconduct Risk?Cross-Border Data Transfers and the EU-US Data Privacy Tug of WarReducing Cybersecurity Burdens with a Customized Data Breach WorkflowTackling Modern Attachment and Link Challenges in G-Suite, Slack, and TeamsThe Convergence of AI and Data Privacy in eDiscovery: Using AI and Analytics to Identify Personal InformationAI, Analytics, and the Benefits of TransparencyCheck them out now or bookmark them to listen to later. Follow the latest updates on Law & Candor and join in the conversation on Twitter. Catch up on past seasons by clicking the links below:Season 1Season 2Season 3Season 4Season 5Special Edition: Impacts of Covid-19For questions regarding this podcast and its content, please reach out to us at info@lighthouseglobal.com.ediscovery-reviewmicrosoft, cybersecurity, analytics, g-suite, ai-big-data, cloud-security, cloud-migration, phi, pii, blog, ediscovery-review,microsoft; cybersecurity; analytics; g-suite; ai-big-data; cloud-security; cloud-migration; phi; pii; bloglighthouse

Self Service

As eDiscovery becomes more complex, organizations are turning to hybrid solutions that give them the flexibility to scale projects up or down as needed. Hybrid solutions offer the best of both worlds: the ability to use self-service, spectra for small matters or full-service for large and complex matters. This flexibility is essential in today's litigation landscape, where the volume and complexity of data can change rapidly. Hybrid solutions give organizations the agility to respond quickly and effectively to changing eDiscovery needs. In a recent webinar, I discussed hybrid eDiscovery solutions with Jennifer Allen, eDiscovery Case Manager at Meta, and Justin Van Alstyne, Senior Corporate Counsel, Discovery and Information Governance at T-Mobile. We explored some of the most pressing eDiscovery challenges, including data complexity, staffing, and implementation. We also discussed scenarios that require flexible solutions, keys to implementing new technology, and the future of eDiscovery solutions. Here are my key takeaways from our conversation.Current eDiscovery challengesA hybrid approach can transition between an internally managed solution and a full-service solution, depending on the nuances and unique challenges of the matter. This type of solution can be beneficial in situations where the exact needs of the case are not known at the outset. A few challenges come into play when deciding your approach to a project:Data volume: When dealing with large data sets, being able to scale is critical. If the data for a matter balloons beyond the capacity of an internal team, having experts available is critical to avoid any disruptions in workflows or errors.Data predictability: When it comes to analyzing data, consistency and predictability can greatly inform your approach to analysis. Standard data allows for more flexibility, as there is an expectation that the results will fall within a certain range. However, to ensure accurate representation, caution must be exercised when dealing with complicated big data. It is important to consider variables, potential outliers, and how the data is compiled and presented. Internal capacity: It's important to monitor and manage the internal workload of your team closely. When everyone is already at their maximum capacity, it can be tempting to outsource various tasks to a full-service project manager. Technology can be a more cost-effective and efficient method for filling the gaps.The right talent and knowledge Finding and utilizing the right team in today's competitive labor market can be difficult. A hybrid solution can help with this by providing a scalable way to get the most out of your workforce. With a hybrid solution, you have the option to staff fewer technical positions and provide training on the data or matters your organization most frequently encounters with your existing team. But, if you have a highly complicated data source, you can still staff an expert who knows how to handle that data. An expert can shepherd the data into a solution, do extensive quality control to ensure that you marry up the family relationships correctly, and give confidence that you're not making a mistake.To assuage concerns about the solution being misused, technology partners can provide training and education, and limit access to who can create, edit, or delete projects within the tool. This training helps to upskill your team by teaching them more advanced technology, which leads to more efficient and sophisticated approaches to matters.Flexible solutions for different mattersA hybrid solution can be a great option for a variety of matters, including internal investigations, enforcement matters, third-party subpoenas, and case assessments. These matters can benefit from the flexibility and scalability provided by a hybrid approach.When determining if a matter needs full-service treatment, it's important to consider the specific requirements at hand. Questions around the volume and frequency of data production, the types of data involved, and the necessary metadata and tagging all play a role in determining if a self-service, spectra approach will suffice or if full-service support is needed. It's always important to consider the timeline and potential challenges during the transition. Using experience with similar cases can provide valuable insight into what might work best in your situation.Keys for implementing eDiscovery solutionsThere are a few critical components to keep in mind when evaluating which eDiscovery solutions and tools are right for your business now, and as it grows.Training team: With any new solution or product there may be some trepidation around learning and adoption. Leverage vendor support to answer your questions and help train your team. Keep them involved in your communications with outside counsel and internal teams so you can receive suggestions and assistance if needed. As users get more experience with the software, they will begin to feel empowered and understand how the tool can be used most effectively. Scalability: One of the most significant hurdles to scaling big eDiscovery projects is the amount of data that needs to be processed. With new data sources, tighter deadlines, and more urgency, it can be difficult to keep up with the demand. Using a fully manual process or a project management solution has a greater chance for error or increased cost. A flexible solution can help your team keep up with increasing data volumes while reducing costs and errors. Automation: Automating repetitive tasks and workflows can dramatically speed up data collection and analysis. This can be a huge advantage when investigating large, complex cases. Additionally, automation can help to ensure that data is collected and parsed consistently.Cost-benefit analysis: Through support and training with a self-service, spectra tool, you can work to reduce the number of support requests. This can minimize the time your team spends on each request and ultimately lowers the cost of providing support. The cost reduction of self-service, spectra tools is often substantial, and it can have a positive snowball effect as your team becomes more skilled at the task. You can reinvest those savings into other business areas with less need for oversight and fewer mistakes. The future state of eDiscovery solutionsThe proliferation of DIY eDiscovery solutions has made it easier for organizations to take control of their data and manage their cases in-house. As AI technology, including continuous active learning (CAL) and technology-assisted review (TAR), continues to evolve, teams will better understand how to handle the growing demands of data and implement hybrid tools. As we move into the future of eDiscovery and legal technology, DIY models will play an increasingly important role in supporting business needs.ediscovery-review; ai-and-analytics; lighting-the-path-to-better-ediscoveryself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; blogself-service, spectrapaige hunt

Complexity, resiliency, adaptability are some of the defining traits from the past year, with good reason. From navigating hybrid workplaces and new collaboration platforms to weathering economic uncertainty and new regulatory postures, the legal industry, and workforce overall, have faced extraordinary challenges. But, with the adversity came great innovation and problem solving. Developments in communications, artificial intelligence (AI), and data have opened new, and often unexpected, possibilities for years to come.With these dynamics in mind, below we've compiled some of our best thinking from 2021, covering critical topics in eDiscovery such as data privacy, AI, information governance, analytics, privilege, and big data. Explore the posts below for some fresh thinking to enter a new year:Navigating the Intersections of Data, Artificial Intelligence, and PrivacyMaking the Case for Information Governance and Why You Should Address it Now Is Your AI Algorithm Admissible in Court? Some Things to ConsiderBig Data Challenges in eDiscovery (and How AI-Based Analytics Can Help)Privilege Mishaps and eDiscovery: Lessons LearnedFour Ways a SaaS Solution Can Make In-House Counsel Life Easierediscovery-reviewblog, ediscovery-reviewbloglighthouse

Are you a healthcare provider or payor with any of these concerns?Your company is trying to manage its budget for litigation and investigations but can’t find the most effective approachYou’re concerned that you may be missing critical insights because you can only review a small subset of your document population to stay within your budgetYou’re subject to an investigation and you want to quickly understand if the government or opposing party has any “gotcha” informationYou want to proactively perform a risk assessment to monitor for fraudulent activitiesIf so, you’re not alone. These are challenges that depend on finding pertinent facts, many of which are buried in the volumes of electronic information most companies now have, quickly and efficiently. In the healthcare industry especially, where litigation and investigation risks are common, complex data environments can pose confounding obstacles to finding key information quickly.In the case of any litigation or investigation, it pays to be able to hit the ground running. Early and effective fact-finding can provide valuable insights for both company and counsel, enabling cost-effective resource alignment based on the strength of the case and faster development of the narrative.Since most insight comes from an assessment of facts that lie within electronically-stored information (ESI), advance preparation for data preservation and collection is critical. So is having the right methodology, tools and expertise in place to find key information once you’ve identified the most important data to explore. Here’s how to optimize those efforts.It's all about data. Plan accordingly.In today’s complex healthcare data landscape, knowing (and finding) the key documents and other information located within massive data collections is no mean feat. Although many data repositories in an enterprise are contained and accessible, today’s myriad data sources, from mobile devices to billing systems to sensor data, are growing in size and complexity every day. Advance planning can speed up the process and enable straightforward and beneficial negotiations with the opposing counsel or regulatory agency.What to do? In advance of litigation or investigation, make sure the enterprise maintains an inventory of data systems that includes descriptions of business owners, users, locations, functionality, backups, data types, possible PII/PHI, and a potential preservation/collection approach. Counsel and in-house legal teams should work with IT to organize this information in a format that can be useful for eDiscovery to enable an expeditious and organized response to a matter.Then make sure that you have the right experts to preserve/collect data from the implicated sources. You may need forensic collections or different ways to extract relevant information from certain data stores. Databases and other structured data sources may require reporting rather than collection techniques, for example, and it’s best to know that early, when you can inform and negotiate with the requesting agency or other side, setting expectations and mitigating potential conflict.Finding key documents quickly is essential. Scrap an out-of-date workflow and explore new methods and tools. There are complex needs involved in a litigation or investigation response and a dizzying array of service providers, tools and technologies to choose from, with new ones being offered every day. The traditional workflow of finding key documents—developing keyword search terms to cull the documents then performing a manual review—is just not efficient. New data analytics and machine learning tools (not to mention the experts that provide them) have opened up a whole new fact-finding horizon. Imagine a team of linguists and search experts with experience in the healthcare domain attacking a complex data population with advanced search and analytics tools going after key documents right from the very start. Actually experiencing how experts leverage such tools to accelerate time to critical insights may be eye-opening for any legal teams who have had to spend weeks and months trying to piece the facts together.What to do? If you haven’t explored new ways to find key documents, you’re probably bogged down with an out-of-date workflow. Pairing advanced analytics tools with the right expertise can accelerate fact-finding and document review, but you may have to try it to believe it. You could discover that having the right expertise on hand in advance of the need will expedite response efforts, reduce cost and risk, and lead to the best possible outcome.Learn more about finding facts fast with Key Document Identification. ediscovery-reviewblog, -investigations, key-document-identification, fact-finding, healthcare-litigation, healthcare-investigations, ediscovery-review,blog; key-document-identification; fact-finding; healthcare-litigation; healthcare-investigationslighthouse

To keep up with the big data challenges in modern review, adopting a technology-enabled approach is critical. Modern technology like AI can help case teams defensibly cull datasets and gain unprecedented early insight into their data. But if downstream document review teams are unable to optimize technology within their workflows and review tasks, many of the early benefits gained by technology can quickly be lost.In a recent episode of Law & Candor, I was happy to discuss the ongoing evolution of document review—including the challenges of incorporating available technologies. We explored some of the most pressing eDiscovery challenges, including today’s data complexity, and how to break through the barriers that keep document review stuck in the manual, linear review model. We also discussed the value of expertise and where it may be applied to optimize review in various phases of a project. Here are my key takeaways from our conversation.Increasing data complexity challenges and entrenched manual review paradigms Today’s digital data—a wellspring of languages, emojis, videos, memes, and unique abbreviations—looks nothing like the early days of electronic information, and it is certainly a universe away from the paper world where legal teams had to plow through documents with paper cuts, redaction tape, and all. Yet, that “paper process” thinking—the manual, linear review model—still has a firm hold in the legal community and presents an unfortunate barrier to optimizing review. The evolution is telling. As digital data began to take over, the early AI adopters and the “humans need to look at everything” review camps staked their ground. Although the two are moving closer together as time goes on, the use of technology is not as highly leveraged as it could be, leaving clients to pay the high costs of siloed review when technology-enabled processes could enhance accuracy and reduce costs. There are a variety of factors that can contribute to this resistance, but it may also be simply a matter of comfort; it’s always easier to do what you already know in the face of changes that may seem too difficult or complex to contemplate. For the best result, know when and where to leverage available technologies in the review process Human beings are certainly a core component of the document review process, and they always will be, but thinking about the entire review lifecycle strategically, from collection through trial preparation, is critical when it comes to understanding where you can gain value from technology. Technology should be considered a supplement to—not a substitute for—human assessment and knowing where to use it effectively is important. When considering the overall document review process, two key questions are: Where can you get more value by using technology? And where are the potential areas of either nuanced or high-risk communications that may require a more individualized assessment? The goal, after all, isn’t to replace humans with technology, but rather to replace outmoded contract review factories with smarter alternatives that leverage the strengths of both technology and human expertise. A smaller review team, coupled with experts who can effectively apply machine learning and linguistic modeling techniques in the right place, is a much more efficient and cost-effective approach than simply using a stable of reviewers. Technology buyers need to understand what a given tech does, how it differs from other products, and what expertise should be deployed to optimize its use Ironically, the profusion of viable tech options that can applied to expedite document review may be off-putting, but this is a “many shades of gray” situation. Many products do similar things and it is important to understand what the differences are—they may be significant. Today’s tools are quite powerful and layering them alongside the TAR tools that document review teams have become more familiar with is what allows for the true optimization of the review process. These tools are not plug-and-play, however. You need to know what you’re doing. It takes specific expertise to be able to assess the needs of the matter, the nature of the data, the efficacy of the appropriate tools, and whether they’re providing the expected result. Collaboration is still the critical core component of document reviewAnd let’s not forget that document review is a collaborative process between client counsel, project managers, and the review team. Within this crucial collaboration, specific expertise at various points in the process ensures the best result, including: • Expertise in review consulting to assess the right options for both the data that’s been collected and the project goals.• Individualized experts in both the out-of-the-box TAR technology as well as any proprietary technology being used so that the tech can be fine-tuned to optimize the benefits.• A core team of expert human reviewers with the appropriate skills.Experimentation with technology can help bridge the divideWith so many products available to enhance the document review workflow, it makes sense to test potential options. Running a parallel process for a particular aspect of the review to get comfortable with a new product can be very helpful. For example, privilege review, which is an expensive part of the review process, could be a good place to test an alternate workflow. An integrated approach works bestThe bottom line is that an integrated approach, advanced technology, and human expertise, is the best solution. The technology to increase the efficiency and effectiveness of document review is out there and most of it has been shown to be low risk and high value. The cost-effectiveness of an integrated approach has been shown over and over again: In using the appropriate technology, budgets can be reduced, and savings reinvested in new matters. It is up to the client and their legal and technology teams to work together in deciding what combination of tools makes the most sense for their organization and matter types. Just make sure to call upon those with the appropriate expertise to provide guidance. For more examples of how AI and human expertise are optimizing review, check out our review solutions page. ai-and-analytics; ediscovery-reviewai-big-data, blog, managed-review, ai-and-analytics, ediscovery-reviewai-big-data; blog; managed-reviewmary newman

A time-saving tool that consolidates different names for the same entity can make all the difference. One of the many challenges of electronic information and messaging rests in ascertaining the actual identity of the message creator or recipient. Even when only one name is associated with a specific document or communication, the identity journey may have only just begun.The many forms our monikers take as they weave in and out of the digital realm may hold no import for most exchanges, but they can be critical when it comes to eDiscovery and privilege review, where accurate identification of individuals and/or organizations is key.It’s difficult enough when common names are shared among many individuals (hello, John Smith?), but the compilation of our own singular name variations and aliases as they live in the realm of digital text and metadata make life no less complicated. In addition, the electronic format of names and email addresses as they appear in headers or other communications can also make a difference. Attempts to consolidate these variations when undertaking document review is painstaking and error-prone.Not metadata — people. Enter “name normalization.” Automated name normalization tools come to the rescue by isolating and consolidating information found in the top-level and sub-level email headers. Automated name normalization is designed to scan, identify, and associate the full set of name variants, aliases, and email addresses for any individual referenced in the data set, making it easier to review documents related to a particular individual during a responsive review.The mindset shift from email sender and recipient information as simply metadata to profiles of individuals is a subtle but compelling one, encouraging case teams and reviewers to consider people-centric ways to engage with data. This is especially helpful when it comes to identifying what may be—and just as importantly what is not—a potentially privileged communication.Early normalization of names can optimize the privilege workflow.When and how name normalization is done can make a big difference, especially when it comes to accelerating privilege review. Name normalization has historically been a process executed at the end of a review for the purpose of populating information into a privilege log or a names key. However, performing this analysis early in the workflow can be hugely beneficial.Normalizing names at the outset of review or during the pre-review stage as data is being processed enables a team to gain crucial intelligence about their data by identifying exactly who is included in the correspondence and what organizations they may be affiliated with. With a set of easy-to-decipher names to work with instead of a mix of full names, nicknames, initials without context, and other random information that may be even more confusing, reviewers don’t have to rely on guesswork to identify people of interest or those whose legally-affiliated or adversarial status may trigger (or break) a privilege call.Name normalization tools vary, and so do their benefits. Not all name normalization tools are created equal, so it is important to understand the features and benefits of the one being used. Ideally, the algorithm in use maximizes the display name and email address associations as well as the quality and legibility of normalized name values, with as little cleanup required as possible. Granular fielded output options, including top level and sub-header participants is also helpful, as are simple tools for categorizing normalized name entities based on their function, such as privilege actors (e.g., in-house counsel, outside counsel, legal agent) and privilege-breaking third parties (e.g., opposing counsel, government agencies). The ability to automatically identify and classify organizations as well as people (e.g., government agencies, educational institutions, etc.) is also a timesaver.Identification of privilege-breaking third parties is important: although some third parties are acting as agents of either the corporation or the law firms in ways that would not break privilege, others likely would. Knowing the difference can allow a team to triage their privilege review by either eliminating documents that include the privilege breakers from the review entirely, significantly reducing the potential privilege pile, or organizing the review with this likelihood in mind, helping to prevent any embarrassing privilege claims that could be rejected by the courts.Products with such features can provide better privilege identification than is currently the norm, resulting in less volume to manage for privilege log review work later on and curtailing the re-reviews that sometimes occur when new privilege actors or breakers come to light later in the workflow. This information enables a better understanding of any outside firms and attorneys that may not have been included in a list of initial privilege terms and assists in prioritizing the review of documents that include explicit or implied interaction with in-house or outside counsel.Other privilege review and logging optimizers. Other analytics features that can accelerate the privilege review process are coming on the scene as AI tools become more accepted for document review. Privilege Analytics from within Lighthouse Matter Analytics can help review teams with this challenging workflow, streamlining and prioritizing second pass review with pre-built classifiers to automate identification of law firms and legal concepts, tag and tier potentially privileged documents, detect privilege waivers, create privilege reasons, and much more.Interested in how Name Normalization works in Privilege Analytics? Let us show you!ediscovery-review; ai-and-analyticsblog, name-normalization, privilege-review, ediscovery-review, ai-and-analyticsblog; name-normalization; privilege-reviewlighthouse

In a recent post, we discussed that requesting parties often demand more transparency with a Technology Assisted Review (TAR) process than they do with a process involving keyword search and manual review. So, how do you get started using (and understanding) TAR without having to defend it? A fairly simple approach: start with some use cases that don’t require you to defend your use of TAR to outside parties.Getting Comfortable with the TAR WorkflowIt’s difficult to use TAR for the first time in a case for which you have production deadlines and demands from requesting parties. One way to become comfortable with the TAR workflow is to conduct it on a case you’ve already completed, using the same document set with which you worked in that prior case. Doing so can accomplish two goals: You develop a better understanding of how the TAR algorithm learns to identify potentially responsive documents: Based on documents that you classify as responsive (or non-responsive), you will see the algorithm begin to rank other documents in the collection as likely to be responsive as well. Assuming your review team was accurate in classifying responsive documents manually, you will see how those same documents are identified as likely to be responsive by the algorithm, which engenders confidence in the algorithm’s ability to accurately classify documents. You learn how the TAR algorithm may identify potentially responsive documents that were missed by the review team: Human reviewers are only human, and they sometimes misclassify documents. In fact, many studies would say they misclassify them regularly. Assuming that the TAR algorithm is properly trained, it will often more accurately classify documents (that are responsive and non-responsive) than the human reviewers, enabling you to learn how the TAR algorithm can catch mistakes that your human reviewers have made.Other Use Cases for TAREven if you don’t have the time to use TAR on a case you’ve already completed, you can use TAR for other use cases that don’t require a level of transparency with opposing counsel, such as: Internal Investigations: When an internal investigation dictates review of a document set that is conducive to using TAR, this is a terrific opportunity to conduct and refine your TAR process without outside review or transparency requirements to uphold. Review Data Produced to You: Turnabout is fair play, right? There is no reason you can’t use TAR to save costs reviewing the documents produced to you to while determining whether the producing party engaged in a document dump. Prioritizing Your Document Set for Review: Even if you plan to review the entire set of potentially responsive documents, using TAR can help you prioritize the set for review, pushing documents less likely to be responsive to the end of the queue. This can be useful in rolling production scenarios, or if you think that eventual settlement could obviate the need to reduce the entire collection.Combining TAR technology with efficient workflows that maximize the effectiveness of the technology takes time and expertise. Working with experts who understand how to get the most out of the TAR algorithm is important. But it can still be daunting to use TAR for the first time in a case where you must meet a stringent level of defensibility and transparency with opposing counsel. Applying TAR to use cases first where that level of transparency is not required enables your company to get to that efficient and effective workflow—before you have to prove its efficacy to an outside party.ediscovery-review; ai-and-analyticstar-predictive-coding, ediscovery-review, ai-and-analyticstar-predictive-codingmitch montoya

When you’re a kid, you love easy games to learn and play, whether they’re interactive games, board games or card games. One of the first card games many kids learn how to play is “Go Fish.” It’s easy to learn because you simply ask the other player if they have any cards of a certain kind (e.g., “got any Kings?”) – if they do, you collect those cards from them; if they don’t, they say “Go Fish” and you have to draw a card from the deck and your turn ends. Easy, right?Conducting keyword searching without a planned, controlled process that includes testing and verifying the results is somewhat like playing “Go Fish” – you might get lucky and retrieve the documents you need to support your case (without retrieving too many others) and you might not. Yet many lawyers and legal professionals think they “get” keyword searching. Why? Because they learned keyword searching in law school using Westlaw and Lexis? Or they understand how to use “Google” to locate web pages related to their topics? But these examples are designed to identify a single item (or handful of items) related to one topic that you seek.Keyword searching for electronic discovery is about balancing recall and precision to produce a proportional volume of electronically-stored information (ESI) that is responsive to the case, which could be thousands or even millions of responsive documents, depending on the issues of the case.Five Common Keyword Searching MistakesWith that in mind, here are five common mistakes that lawyers and legal professionals make when conducting keyword searches:1. Poor Use of Wildcards: Wildcard characters can be helpful in expanding the scope of the search, but only if you use them well — and understand how they are applied by the search engine you’re using (warning: don’t use Google’s search engine as an exemplar). Poorly placed or ill-advised wildcard character(s) can completely blow up a search. A few years ago, there was a case where one of the goals was to identify documents that related to apps on devices (mobile and PC), so the legal team decided to use a search term “app*” to retrieve words like “app”, “application”, “apps”, etc. Great, right? Not when that same term also retrieves terms like “appear”, “apparent”, “applied”, “appraise”, etc. A better search in this case would have been (app or apps or application*). Make sure to think through word variability and consider word formulations that could be hit by the search. Also consider whether wildcard operators are attached at the appropriate place in the stem of a word so that all of the variants are hit. If not, the search might target too many unrelated words or omit words you want to capture.2. Use of Noise or Stop Words: To keep retrieval responsive even in large databases, most platforms don’t index certain common words that appear regularly (defined as “noise” or “stop” words), yet many legal professionals fail to exclude these noise words in the searches they conduct – yielding unexpected results. Search terms such as “management did” or “counseled out” won’t work if “did” and “out” are noise words that can’t be retrieved. There are typically 100 or more words that are not indexed by a typical platform, so it’s important to understand what they are and plan around them in creating searches that can get you as close as possible to your desired result.3. Starting with Searches That Are Too Broad: Another common mistake is to start with searches that are too broad, assuming that you’ll get a result that will be easy to narrow down through additional search. In fact, you may get a result that makes it nearly impossible to determine what might be causing your search to retrieve unexpected results. Keyword search works best when the hard work has been done up front, either by working with subject matter experts who have provided insight into likely vocabulary used (e.g., shorthand, code words, slang) or via a targeted exploration of the document population. That knowledge, coupled with the effective use of Boolean operators like AND, OR, and NOT, should enable you to craft initial searches that put targeted words in the appropriate context, increasing the likelihood that relevant material will be found at the outset. That result will provide the necessary fodder for developing additional searches that are more precise.4. Failing to Test What’s Retrieved: Many legal professionals create a search, perform that search and then proceed to review without testing the results. Performing a random sample on the results could quickly identify a search that is considerably overbroad and would result in a low prevalence rate of responsive documents, driving up costs for review and production. Testing the result set to ensure the search is properly scoped is well worth the time and effort to take that extra step in terms of potential cost savings. Better to review an extra few hundred documents than an extra hundred thousand documents.5. Failing to Test What’s Not Retrieved: It’s just as important to test the documents that were not retrieved in a search to identify areas that were potentially missed. Not only does a random sample of the “null set” help identify searches that were too narrow in scope, they also are important in addressing defensibility concerns related to your search process if it is challenged by opposing counsel.The ”Go Fish” analogy isn’t an original one – then New York Magistrate Judge Andrew J. Peck used it in his article Search, Forward over nine years ago (October 2011) when he observed that “many counsel still use the “Go Fish” model of keyword search.” If you’re making some of the mistakes listed above, you might be doing so as well. Proper keyword searching is an expert planned and managed process that avoids these mistakes to maximize the proportionality and defensibility of your discovery process. It’s not a kid’s game, so make sure you don’t treat it like one.ediscovery-reviewblog, -keyword-search, ediscovery-review,blog; keyword-searchlighthouse

Your team is facing a wall of mounting compliance requirements and internal investigations, as well as a few larger litigations you fear you may not be able to handle given internal resource constraints. Each case involves unwieldy amounts of data to wade through, and that data must be collected from constantly-evolving data sources—from iPhones to Microsoft Teams to Skype chats. You’re working with your IT team to ensure your company’s most sensitive data is protected throughout the course of all those matters.All of this considered, your team is faced with vetting eDiscovery vendors to handle the large litigation matters and ensuring those vendors can effectively protect your company’s data. Simultaneously, you are shouldering the burden of hosting a separate eDiscovery platform for internal investigations with a legal budget that is already stretched thin. Does this sound familiar? Welcome to the life of a modern in-house attorney. Now more than ever, in-house counsel need to identify cost-effective ways to improve the effectiveness and efficiency of their eDiscovery matters and investigations with attention to the security of their company’s data. This is where adopting a cloud-based self-service, spectra eDiscovery platform can help. Below, I’ve outlined how moving to this type of model can ease many of the burdens faced by corporate legal departments.1. The Added Benefit of On-Demand Scalability‍A cloud-based, self-service, spectra platform provides your team the ability to quickly transfer case data into a cutting-edge review platform and access it from any web browser. You’re no longer waiting days for a vendor to take on the task with no insight into when the data will be ready. With a self-service, spectra solution, your team holds the reigns and can make strategic decisions based on what works best for your budget and organization. If your team has the bandwidth to handle smaller internal investigations but needs help handling large litigations, a scalable self-service, spectra model can provide that solution. If you want your team to handle all matters, large and small, but you worry about collecting from unique sources like Microsoft Teams or need help defensibly culling a large amount of data in a particular case, a quality self-service, spectra provider can handle those issues and leave the rest to you. In short, a self-service, spectra solution gives you the ability to control your own fate and leverage the eDiscovery tools and expertise you need, when you need them. 2. Access to the Best eDiscovery Tools – Without the Overhead Costs A robust self-service, spectra eDiscovery solution gives your team access to the industry’s best eDiscovery tools, enabling you to achieve the best outcome on every matter for the most efficient cost. Whether you want to analyze your organization’s entire legal portfolio to see where you can improve review efficiency across matters, or you simply want to leverage the best tools from collection to production, the right solution will deliver. And with a self-service, spectra model, your team will have access to these tools without the burden of infrastructure maintenance or software licensing. A quality self-service, spectra provider will shoulder these costs, as well as the load of continuously evaluating and updating technology. Your team is free to do what it is does best: legal work.3. The Peace of Mind of Reliable Data Security In a self-service, spectra eDiscovery model, your service provider shoulders the data security risk with state-of-the-art infrastructure and dedicated IT and security teams capable of remaining attentive to cybersecurity threats and evolving regulatory standards. This not only allows you to lower your own costs and free up valuable internal IT resources, but also provides something even more valuable than cost savings—the peace of mind that comes with knowing your company’s data is being managed and protected by IT experts.4. Flexible, Predictable Pricing and Lower Overall Costsself-service, spectra pricing models can be designed around your team’s expectations for utilization—meaning you can select a pricing structure that fits your organization’s unique needs. From pay-as-you-go models to a subscription-based approach, self-service, spectra pricing often differs from traditional eDiscovery pricing in that it is clear and predictable. This means you won’t be blindsided at the close of the month with hidden charges or unexpected hourly fees from a law firm or vendor. Add this type of transparent pricing to the fact that you will no longer be shouldering technology costs or paying for vendor services you don’t need, and the result is a significantly lower eDiscovery overhead that can fit within any legal budget. These four benefits can help corporations and in-house counsel teams significantly improve eDiscovery efficiency and reduce costs. For more information on how to move your organization to a self-service, spectra eDiscovery model, be sure to check out our other articles related to the self-service, spectra eDiscovery revolution – including tips for overcoming self-service, spectra objections and building a self-service, spectra business case.ediscovery-review; ai-and-analyticsself-service, spectra, blog, ediscovery-review, ai-and-analyticsself-service, spectra; bloglighthouse

Planning the best ways to find key documents will pay off in the long run. Getting to the bottom of alleged claims is often a high-stakes race to find critical information amidst an avalanche of data. Regardless of whether you are conducting an internal investigation, early case assessment, or preparing for depositions, there is no time to waste. Although it’s surely tempting to dive right into document reviews to find the key documents that will shed light on the matter at hand, litigators and investigators know that good preparation leads to a better result.Conducting fact-finding in a reactive manner by skipping upfront preparation diminishes the ability to systematically investigate the full set of allegations and compromises the development of a comprehensive factual narrative. Here are a few things to keep in mind as you prepare.Consider the source(s).To conduct efficient fact-finding through key document identification, you need to first take stock of the various sources of data available for review and then map them to the type of evidence they may contain.Is the evidence you are looking for likely to reside in reports, communications, or memos? Are there particular sets of custodial data that are likely more important to understanding the case than others? Are inbound consumer marketing solicitations to employees, or bulk email news alerts likely to contain important information for the case? Taking the time to consider these questions and articulate hypotheses about where important evidence may reside allows you to effectively prioritize which data sets to search through first.What are the targets?In addition to prioritizing the data, it’s critically important to articulate the array of evidence you are looking for based on the set of allegations at issue. Your understanding of the case will certainly evolve as fact-finding progresses, but defining evidentiary targets in advance better enables you to assess later on whether you have diligently investigated all possible angles. Moreover, defining discrete targets for fact-finding allows you to articulate searches at a more granular level. Rather than leveraging one fully encompassing crude keyword search to hunt for key documents, creating a net of many targeted searches will lead to more comprehensive results in a more efficient manner.What tools should you use?Another key to efficient and successful fact-finding is selecting the right data analytics tools that will help reduce the noise and boost the signal. For example, threading email conversations and identifying near-duplicate sets of documents are two of the many approaches available to winnow down and prioritize the set of documents you perform targeted searches on. Techniques such as name normalization can also be especially helpful when your aim is to understand who is communicating with whom about which underlying facts. It might even be worth investigating how to best tailor the way the data is indexed for searching — for instance, emojis are often used in key conversations useful in investigations yet they are rarely indexed for search in review platforms unless you explicitly specify them to be.Understanding the data, articulating an evidentiary approach, and equipping yourself with the right data analytics helps ensure that critical facts do go undiscovered. Although it’s natural to want to get right into the thick of it, skilled counsel know that high-stakes fact-finding is a complex affair requiring forethought and preparation. And once in place, a well-informed search strategy can be quickly executed allowing your team to spend more time understanding the significance of key documents, and less time re-evaluating and tinkering with approaches for finding them.Want to know more? Watch “Winning the Race for the Facts: Case Studies on How to Leverage Technology and Search Expertise for Investigations and Case Preparation,” a joint webinar with H5 and Covington & Burling, for further tips on finding key documents for investigations.ediscovery-reviewblog, -key-document-identification, kdi, ediscovery-review,blog; key-document-identification; kdilighthouse

Evolving analytics tools and methods can help expedite review.Analyze this! No, we’re not talking about the 1999 movie starring Robert DeNiro and Billy Crystal, but rather analytics mechanisms that many organizations are using today to streamline discovery. As these mechanisms become more sophisticated, it pays to keep abreast of the ways in which they can impact a review, including how data can be organized, visualized, identified and reduced.For example, conceptual clustering can identify groups of topics that might be clearly responsive or non-responsive. Communication visualization maps can identify communication patterns of key parties within a data collection And, of course, predictive coding can train a supervised machine learning algorithm to identify potentially responsive and non-responsive documents based on classifications of other documents.But there are other use cases for eDiscovery analytics many organizations aren’t taking advantage of that make eDiscovery workflows even more efficient and more cost effective. To improve the efficiency of eDiscovery workflows, organizations can now implement technology with the following analytics features.Email Threading and Near Duplicate IdentificationYou may have heard the famous phrase “Insanity is doing the same thing over and over again expecting a different result.” But, in document review, insanity is simply doing the same thing over and over again. De-duplication using hash values identifies documents that are exact duplicates in content and format, but there is considerable additional content within document collections that is also duplicated within documents that aren’t exact matches. Email conversation threads contain considerable duplicative information, but conversations between multiple people can branch off, so you can’t just assume that the last message for the thread contains the entire thread discussion.Documents converted to PDF may be identical in content but not format, so they have different hash values and are not “de-duped.” ESI collections often include multiple drafts of documents that have both duplicative and unique content. To avoid over-capture of duplicates and gain visibility into email branches, organizations can now employ advanced analytics that can help in the following ways:Utilize advanced algorithms to identify email thread relationships and individual emails in a thread with unique contentGroup similar documents with flexible near-duplicate identification to easily review and compare to determine whether the differences are significantIdentify exact content duplicates with only formatting differences that hash de-duplication would not catch.Name Normalization and Entity AnalysisWhat’s in a name? Potentially, a whole lot of options! If the sixth US president were alive today and sending emails, here are some ways that you might see him represented within the collection:John AdamsJohnny AdamsJohn Q. AdamsQ. AdamsQuincy AdamsAdams, JohnAdams, John Q.Adams, J.Q.Adams, J. Quincyjadams@xyzcorp.com/O=XYZCORP/OU=EXCHANGE ADMINISTRATIVE GROUP (FYDIBOHF23SPDLT)/CN=RECIPIENTS/CN=jadamsAdams@gmail.comAnd potentially more…That’s a lot of variation – just for one person! Case teams often waste significant time and energy sorting through the numerous variations of names and email addresses for individuals in a matter. Advanced analytics solutions can be used to automated name normalization algorithms to link different name variations and email addresses to a single individual, format those names uniformly and aggregate the normalized participants that appear across an entire email thread group. The result? Refined results that streamline processes such as privilege logging without the intensive manual cleanup typically associated with the process.Metadata AnalyticsAI-driven analytics applied to the metadata can streamline eDiscovery by:a) identifying mass email communications so that reviewers can focus on more likely responsive emails;b) filtering email signature images and other extraneous embedded objects; andc) remediating data populations with missing or incomplete metadata by auto-detecting and populating email metadata fields on inbound productions.Privilege AnalyticsAutomated categorization and classification powered by advanced analytics can also be applied to privilege review to weed out non-responsive and non-privileged material early and rapidly identify, elevate and prioritize potentially privileged information. Customizable rules to exclude disclaimers and boilerplate language can also improve the accuracy of that identification process by eliminating many false positives.As most privilege determinations involve considerations of nuance and context, human judgments are a necessary part of the process. Pre-built and customized linguistic models, name normalization and email thread identification can extend those automated privilege determinations more quickly through the collection, with automated identification of legal concepts, privilege actors and law firms and a reusable asset with consistent propagation of privilege designations across matters.And clean name normalization outputs, along with automated and customizable privilege reasons assigned to each document expedite privilege log creation, significantly decreasing the manual cleanup often associated with this time-consuming task.Personal Identifiable Information (PII) DetectionFinally, with all of the data privacy requirements associated with recent regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), identifying and protecting PII has become a requirement within every phase of the eDiscovery lifecycle. Using analytics and pattern matching through regular expressions (RegEx) to identify common format numbers such as passport IDs, social security numbers, drivers license numbers and credit card numbers, as well as identification of common form types that often contain PII (such as loan applications or IRS forms) will help flag those documents so that they can be adequately protected throughout the process.Newer, more advanced AI-driven analytics solutions go a step further by utilizing highly precise classifiers to model the way in which different forms of supported personal data appear in data populations. These automated solutions provide rapid identification of likely and potential PII, resulting in rapid insights and immediate access to the most relevant documents first.ConclusionYou may be using analytics to streamline parts of your eDiscovery process, but there are always new use cases being identified to leverage analytics to make your eDiscovery workflows more efficient. Even Analyze This had a sequel!For more information on ways H5 Matter Analytics® can assist your organization in creating efficiencies and expediting eDiscovery workflows, click here.ediscovery-reviewblog, -ediscovery, data-analytics, document-review, ediscovery-review, aiandanalyticsblog; ediscovery; data-analytics; document-reviewlighthouse

AI & Analytics

Artificial intelligence (AI) has proliferated across industries, in popular culture, and in the legal space. But what does AI really mean? One way to look at it is in reference to technology that lets lawyers and organizations efficiently manage massive quantities of data that no one’s been able to analyze and understand before.While AI tools are no longer brand new, they’re still evolving, and so is the industry’s comfort and trust in them. To look deeper into the technology available and how lawyers can use it Lighthouse hosted a panel featuring experts Mark Noel, Director of Advanced Client Data Solutions at Hogan Lovells, Sam Sessler, Assistant Director of Global eDiscovery Services at Norton Rose Fulbright, Bradley Johnston, Senior Counsel eDiscovery at Cardinal Health, and Paige Hunt, Lighthouse’s VP of Global Discovery Solutions.Some of the key themes and ideas that emerged from the discussion include:Defining AIMeeting client expectationsUnderstanding attorneys’ duty of competenceIdentifying critical factors in choosing an AI toolAssessing AI’s impact on process and strategyThe future of AI in the legal industryDefining AIThe term “AI” can be misleading. It’s important to recognize that, right now, it’s an umbrella term encompassing many different techniques. The most common form of AI in the legal space is machine learning, and the earliest tools were document review technologies in the eDiscovery space. Other forms of AI include deep learning, continuous active learning (CAL), neural networks, and natural language processing (NLP).While eDiscovery was a proving ground for these solutions, the legal industry now sees more prebuilt and portable algorithms used in a wide range of use cases, including data privacy, cyber security, and internal investigations.Clients’ Expectations and Lawyers’ DutiesThe broad adoption of AI technologies has been slow, which comes as no surprise to the legal industry. Lawyers tend to be wary of change, particularly when it comes at the hands of techniques that can be difficult to understand. But our panel of experts agreed that barriers to entry were less of an issue at this point, and now many lawyers and clients expect to use AI.Lawyers and clients have widely adopted AI techniques in eDiscovery and other privacy and security matters. However, the emphasis from clients is less about the technology and more about efficiency. They want their law firms and vendors to provide as much value as possible for their budgets.Another client expectation is reducing risk to the greatest extent possible. For example, many AI technologies offer the consistency and accuracy needed to reduce the risk of inadvertent disclosures.Mingled with client expectations is a lawyer’s duty to be familiar with technology from a competency standpoint. We aren’t to the point in the legal industry where lawyers violate their duty of competence if they don’t use AI tools. However, the technology may mature to the point where it becomes an ethical issue for lawyers not to use AI.Choosing the Right AI ToolDecide Based on the Search TaskThere’s always the question of which AI technology to deploy and when. While less experienced lawyers might assume the right tool depends on the practice area, the panelists all focused on the search task. Many of the same search tasks occur across practice areas and enterprises.Lawyers should choose an AI technology that will give them the information they need. For example, Technology-assisted review (TAR) is well-suited to classifying documents, whereas clustering is helpful for exploration.Focus More on FeaturesTeams should consider the various options’ features and insights when purchasing AI for eDiscovery. They also must consider the training protocol, process, and workflow. At the end of the day, the results must be repeatable and defensible. Several solutions may be suitable as long as the team can apply a scientific approach to the process and perform early data assessment. Additional factors include connectivity with the organization’s other technology and cost.The process and results matter most. Lawyers are better off looking at the system as a whole and its features in deciding which AI tech to deploy instead of focusing on the algorithm itself.Although not strictly necessary, it can be helpful to choose a solution the team can apply to multiple problems and tasks. Some tools are more flexible than others, so reuse is something to consider.Some Use Cases Allow for ExperimentationThere’s also the choice between a well-established solution versus a lesser-known technology. Again, defensibility may push a team toward a well-known and respected tool. However, teams can take calculated risks with newer technologies when dealing with exploratory and internal tasks.A Custom Solution Isn’t NecessaryThe participants noted the rise in premade, portable AI solutions more than once. Rarely will it benefit a team to create a custom AI solution from scratch. There’s no need to reinvent the wheel. Instead, lawyers should always try an off-the-shelve system first, even if it requires fine-tuning or adjustments.AI’s Impact on ProcessThe process and workflow are critical no matter which solution a team chooses. Whether for eDiscovery, an internal investigation, or a cyber security incident, lawyers need accurate and defensible results.Some AI tools allow teams to track and document the process better than others. However, whatever the tool’s features, the lawyers must prioritize documentation. It’s up to them to thoughtfully train the chosen system, create a defensible workflow, and log their progress.As the adage goes: garbage in, garbage out. The effort and information the team inputs into the AI tool will influence the validity of the results. The tool itself may slightly influence the team’s approach. However, any approach should flow from a scientific process and evidence-based decisions.AI’s Influence on StrategyThere’s a lot of potential for AI to help organizations more strategically manage their documents, data, and approach to cases. Consider privileged communications and redactions. AI tools enable organizations to review and classify documents as their employees create them—long before litigation or another matter. Classification coding can travel with the document, from one legal matter to another and even across vendors, saving organizations time and money.Consistency is relevant, too. Organizations can use AI tools to improve the accuracy and uniformity of identifying, classifying, and redacting information. A well-trained AI tool can offer better results than people who may be inconsistently trained, biased, or distracted.Another factor is reusing AI technology for multiple search tasks. Depending on the tool, an organization can use it repeatedly. Or it can use the results from one project to the next. That may look like knowing which documents are privileged ahead of time or an ongoing redaction log. It can also look like using a set of documents to better train the algorithm for the next task.The Future of AIThe panelists wrapped the webinar by discussing what they expect for the future of AI in the legal space. They agreed that being able to reuse work products and the concept of data lakes will become even greater focuses. Reuse can significantly impact tasks that have traditionally had a huge cost burden, such as privilege reviews and logs, sensitive data identification, and data breach and cyber incidents.Another likelihood is AI technology expanding to more use cases. While lawyers tend to use these tools for similar search tasks, the technology itself has potential for many other legal matters, both adversarial and transactional. To hear more of what the experts had to say, watch the webinar, “Deploying Modern Analytics for Today’s Critical Data Challenges.” ai-and-analytics; ediscovery-review; lighting-the-path-to-better-ediscoveryai-big-data, blog, data-reuse, project-management, ai-and-analytics, ediscovery-reviewai-big-data; blog; data-reuse; project-managementai-analyticslighthouse

Much of the workforce has been forced into remote work due to social distancing requirements because of the pandemic, and that includes the workforce conducting services related to electronic discovery. Many providers have been forced into remote work for services including collection and review. Other providers have been already conducting those services remotely for years, so they were well prepared to continue to provide those services remotely during the pandemic.Make no mistake, it’s important to select a review provider that has considerable experience conducting remote reviews which extends well before the pandemic. Not all providers have that level of experience. But the success of your reviews isn’t about location, location, location; it’s about process, process, process — and the ability to manage the review effectively regardless of where it’s conducted. Here are four best practices to make your document reviews more efficient and cost effective, regardless of where they’re conducted:Maximize culling and filtering techniques up front: Successful reviews begin with identifying the documents that shouldn’t be reviewed in the first place and removing them from the document collection before starting review. Techniques for culling the document collection include de-duplication and de-nisting and identification of irrelevant domains. But it’s also important to craft a search that maximizes the balance between recall and precision to exclude thousands of additional documents that might otherwise be needlessly reviewed, saving time and money during document review.Combine subject matter and best practice expertise: Counsel understands the issues associated with the case, but they often don’t understand how to implement sophisticated discovery workflows that incorporate the latest technological approaches (such as linguistic search) to maximize efficiency. It’s important to select the provider that knows the right questions to ask to combine subject matter expertise with eDiscovery best practices to ensure an efficient and cost-effective review process. It’s also important to continue to communicate and adjust workflows during the case as you learn more about the document collection and how it relates to the issues of the case.Conduct search and review iteratively: Many people think of eDiscovery document review as a linear process, but the most effective reviews today are those that implement an iterative process that that interweave search and review to continue to refine the review corpus. The use of AI algorithms and expert-designed linguistic models to test, measure and refine searches is important to achieve a high accuracy rate during review, so remember the mantra of “test, measure, refine, repeat” for search and review to maximize the quality of your search and review process.Consider producing iteratively, as well: Discovery is a deadline driven process, but that doesn’t mean you have to wait for the deadline to provide your entire production to opposing counsel. Rolling productions are common today to enable producing parties to meet their discovery obligations over time, establishing goodwill with opposing counsel and demonstrating to the court that you have been meeting your obligations in good faith along the way if disputes occur. Include discussion of rolling productions in your Rule 26(f) meet and confer with opposing counsel to enable you to manage the production more effectively over the life of the project.You’re probably familiar with the famous quote from The Art of War by Sun Tzu that “every battle is won or lost before it is ever fought,” which emphasizes the importance of preparation before proceeding with the task or process you plan to perform. Regardless where your review is being conducted, it’s not the location, location, location that will determine the success of your review, but the process, process, process. After all, it’s called “managed review” for a reason!ediscovery-reviewblog, -document-review, ediscovery-review,blog; document-reviewlighthouse

What is data reuse? There are many different flavors and not everyone thinks about it the same way. In the context of eDiscovery, subject-matter specific work product in the form of responsiveness or issue coding often comes to mind and is then immediately dismissed as untenable given that the definitions for these can change from matter to matter. This is just one tiny piece of what’s possible, however. We need to consider the entire EDRM from end to end. What else has already been done, and what can be gained from it?First, there’s the source data itself. The underlying electronically stored information (ESI) is foundational to the reuse of data as a whole. Many corporations deal with frequent litigation and investigations, and those matters often include the same or at least overlapping players, i.e. the “frequent flier” custodians. This means the same data is relevant to multiple matters, which means it can be reused. There’s the potential for a one-to-many relationship here. In other words, instead of starting from scratch with each new project by going back to the same sources to collect the same data, why not take stock of what has been collected already? Compare the previously collected inventory to what is required for each specific matter, and then return to the well for the difference as needed. It may be as simple as a “refresh” to capture a more recent date range, or, even better, there’s no new collection to be done at all.Next up is the processed data. Once it’s collected, a lot of time, effort, and money are spent transforming ESI into a more consumable format. Extracting and indexing the metadata such that it can easily be searched and reviewed in your platform of choice takes real effort. Considering the lift, utilizing data that has already undergone processing makes a lot of sense. Depending on volume, significant savings in terms of timeline and fees are often realized, and this is not a one-time thing. The same data often comes up over and over across multiple matters, compounding savings over time.Finally, after processing comes review, which is where reusing existing work product comes in. This isn’t limited to relevance calls, which may or may not consistently apply across matters. There’s limited application for the reuse of subject-matter specific work product as mentioned earlier. The real treasure trove is all the different types of static work product – the ones that remain the same across matters regardless of the relevance criteria – and there are so many! One valuable step that is often overlooked is the ability to dismiss portions of the data population upfront. Often there is some chunk of data that will simply never be of interest. These are the “junk” or “objectively non-relevant” files that can clog a review. For example, automatic notifications, spam advertisements, and other mass mailings can contribute a lot of volume and rarely have any chance of including relevant content. Also, think about redactions and what often drives them: PII, PHI, trade secret, IP, etc. These are a pain to deal with, so why force the need to do so repeatedly? And, what about privilege? Identifying it is one thing, and then there are the incredibly time intensive privilege log entries that follow. These don’t change, and the cost to handle them can be steep. On top of that, they are incredibly sensitive, so ensuring accuracy and consistency is key. That’s pretty difficult to accomplish from matter to matter if you rely on different reviewers starting over each time.At the end of the day, no one wants to waste time and effort on unnecessary tasks, especially considering how often intense deadlines loom right out of the gate. The key is understanding what has already been done that overlaps with the matter at hand and leveraging it accordingly. In other words, know what you have and use it to avoid performing the same task twice wherever possible.ai-and-analytics; ediscovery-reviewediscovery-process, data-re-use, blog, ai-and-analytics, ediscovery-reviewediscovery-process; data-re-use; bloglighthouse

Now more than ever, data security has become priority number one, especially in the context of litigation and eDiscovery. And as the worlds of eDiscovery, information governance, and cybersecurity continue to rapidly converge, cybersecurity incidents are alarmingly on the rise, showcasing all of the weaknesses in an organization’s information governance system. Addressing cybersecurity continues to be a top challenge in eDiscovery. Many are unsure if their own internal processes are safe, not to mention those of the vendors who manage their outsourced eDiscovery.So, how can you protect your ESI all the way from preservation and collection to review and production? In a Law and Candor podcast episode, special guest David Kessler, Head of Data and Information Risk at Norton Rose Fulbright US LLP, discussed with our hosts the diverse set of challenges that arise with data security at each stage of the EDRM. Most understand the right methods start with implementing the fundamentals of cybersecurity, but some have learned the hard way that you can’t fix a house built on a shaky foundation after a cybersecurity disaster strikes. With the protection of client ESI first and foremost top of mind, here are the some of the most pressing cybersecurity challenges in eDiscovery as well as actionable solutions.Cybersecurity Challenges in eDiscoveryThe intersection of information governance, eDiscovery, and data security: The nature of data has evolved such that eDiscovery and information governance naturally intersect with data privacy and security. We’ve learned that issues around data access are very similar to eDiscovery issues and the next challenge is learning how to operate the areas together cohesively. In addition, with the shift to scrutiny on privacy and what can be done with personal data, now we know almost all cases that involve ESI have tremendous privacy concerns.The important role eDiscovery plays in cybersecurity: No longer are the days where confidential data relevant to litigation is primarily found in email and simply on computers. Now, data is created and stored across a wide variety of mediums and the amount of data continues to grow at an exponential rate. For cybersecurity criminals, this is a gold mine of confidential data available to steal and access.The outstanding security gaps throughout the EDRM: Historically, we’ve been focused on the responding parties’ obligations to securely undertake discovery. The business process of eDiscovery is primarily about collecting, copying, and transferring data outside of an organization, which creates concerns about securing that information at every stage of the process. Both the responding and requesting parties need to find a way to collaboratively and cooperatively work together at the beginning of a case to ensure data is protected through the entire EDRM lifecycle.The weakest part of the cybersecurity chain is when you hand over sensitive data: How do we help clients make sure their data isn’t accidentally or intentionally taken from them during the eDiscovery process? Everyone from eDiscovery vendors to law firms has an obligation to shore up their security and organizations have a responsibility to thoroughly vet those partners as they hand over their most sensitive data. In the EDRM, attention has shifted to making sure cybersecurity protections span the entire EDRM and the last step that hasn’t received much attention is making sure the requesting party is taking the appropriate steps to secure the data once they receive it.Cybersecurity Solutions in eDiscoveryShore up cybersecurity contracts and repurpose existing security riders: When an organization engages law firms and eDiscovery vendors to handle discovery, it’s important they work closely with their data security IT team. These teams can help to repurpose some of the standard security riders from other contracts and use it to create new contracts with the appropriate protections in place.Establish comprehensive protective orders at the beginning of cases: With respect to the requesting party, who you will ultimately be producing the data to, ensure that early in the case you’ve negotiated a comprehensive protective order that includes reasonable and proportionate requirements for the protection of data. In that protection order (and a step that’s often forgotten), follow up and confirm the data you produced has been deleted after a case is over.Keep open lines of communication with law firms and eDiscovery vendors: Your discovery partners understand and have a significant stake in their security reputations. They have a strong motivation to work with you to execute risk assessments and other agreements that contain the necessary security provisions to ensure your data is safe at every step of the process. Also, include a breach notification order if data is accidentally lost or there’s an attack.Focus on things you can do to strengthen your productions: Think about the most efficient ways to reduce the number of copies involved in productions where appropriate. For example, use redaction as much as possible and consequently less copies of data. Don’t produce sensitive and irrelevant portions of data – redact it instead.Ultimately, most people have become acutely aware of the vulnerabilities that exist in data security as it travels through the EDRM, and as law firms and eDiscovery vendors become accustomed to deeper vetting, it’s at the production stage where the biggest security vulnerabilities seem to remain. To get ahead of all aspects of potential cybersecurity failures, the use of well-written protective orders will get you a long way. Requirements in protective orders can ensure all parties take reasonable steps to protect data from third-party hackers and unauthorized access, as well as include protections based on encryption, access controls, passwords, etc.data-privacy; information-governance; ediscovery-reviewcybersecurity, cloud-security, ediscovery-process, preservation-and-collection, blog, data-privacy, information-governance, ediscovery-review,cybersecurity; cloud-security; ediscovery-process; preservation-and-collection; bloglighthouse

In 2021, corporations and law firms alike grappled with yet another year of disruption and unpredictability caused by economic volatility, a lingering global pandemic, increased regulation, and inequality within the workforce. To help our clients prepare for whatever 2022 may have in store, Lighthouse CEO Brian McManus welcomed economic forecaster and former CNBC chief economist and Squawk Box co-host Marci Rossell for a lively discussion centered around these current global macroeconomic trends, with a focus on their effect on the legal industry.Their conversation was wide-ranging and informative, touching on impacts, causes, and forecasts related to inflation, global workforce shortages, inequality in the workplace, technology adoption, and increased regulatory and data privacy restrictions. The key takeaways from this discussion are outlined below.Economic InflationAs of January 2022, the inflation rate was hovering around 7% in the United States (US), and around 5% in the European Union (EU). These are the highest inflation rates both countries have seen in decades. Rossell explained that one of the major contributing factors for this increase is the speed at which the overall economy recovered from the abrupt halt in economic activity in the spring of 2020 due to the COVID-19 pandemic. The sharp economic recovery drove a surge in demand for services and goods, at a time when supply around the world was at an all-time low due to pandemic-related shutdowns. This tension led to the current sustained inflation rates we’re seeing today, and those rates can be expected to remain high for the foreseeable future in markets where production is not expected to meet demand any time soon (such as the energy and oil industries).Within the legal industry, specifically, law firms and organizations have not only been impacted by the typical “cost of goods” inflation described above – they have also been impacted by inflation related to labor shortages and rising wages, as well as costs related to regulation and compliance. “The Great Resignation” and Its Impact on the Legal IndustryOver the last two years, droves of workers have switched employers, changed careers, or left the workforce all together, in what pundits and economists have deemed, “The Great Resignation.” Rossell explained that this global phenomenon may have roots in the financial crises of 2008 – 2009, when the economy contracted dramatically, leaving millennials struggling to enter a workforce plagued by an unemployment rate that had soared into the double digits. In the wake of this recession and for years afterward, the balance of power between employers and employees was weighted heavily in favor of employers, with overqualified workers applying to the same jobs, giving employers their pick of quality candidates. Now, this same generation of millennials have been confronted with a pandemic that has caused millions of people to suddenly sever their connections to jobs, employers, and/or geography. Many of these workers may not have felt very connected to where they worked or lived in the first place, but stayed because of their previous experience in a job market that was heavily influenced by the last recession. The pandemic suddenly forced this generation of workers into a situation that ultimately enabled them to make different career choices. And we are certainly seeing them making those choices. As Rossell noted, in addition to this trend among the millennial generation, the pandemic also escalated early retirements for an older generation, while an overall decrease in population growth has led to 400,000 fewer young people entering the labor force every year. These three factors are a perfect labor-shortage storm, with fewer experienced workers, fewer young people entering the labor market, and a generation of mid-career millennials reevaluating their careers and/or employers.McManus pointed out that labor shortage has also had a significant effect on the legal industry generally, and the eDiscovery industry specifically. eDiscovery is a niche industry, which makes it harder to find and retain experienced talent in general. But over the last twelve months, the tighter labor market has significantly exacerbated those issues. There is now a shortage of talent within eDiscovery and the cost of retaining valuable talent has sharply increased over the last nine months, with experienced employees being offered 20% to 40% more in compensation.This trend also affects the broader legal industry. Attrition of associates at law firms was at an unprecedented level in 2021 and the cost of retaining associates skyrocketed. For example, law firm associate compensation grew 11% in November of 2021, year over year, according to a state of the legal market report from the Thomson Reuters Institute. This trend can be expected to continue over the next few years due to the economic factors at play.To combat the worker shortage, McManus warned that employers should expect to not only offer higher compensation, but also include benefits like flexible work arrangements, in order to recruit and retain talented employees. Even prior to the pandemic, Rossell noted, studies showed that flexible work arrangement benefits were worth about 8% of a salary to younger employees. This trend is expected to sustain well into the future, as housing market trends indicate that 30-somethings are moving to larger homes away from large corporate offices and cities.Diversity, Equity, and Inclusion in the WorkforceThere has been a significant emphasis placed on diversity, equity, and inclusion (DE&I) over the past few years across many markets, including the legal industry. Rossell provided a historical perspective, explaining that thirty years ago the consensus from economists was that the labor market was rational and profit-maximizing and thus, discrimination in the labor force could not exist. The theory was that for-profit companies would always be incentivized to hire the best individual for the job, regardless of gender, race, ethnicity, sexual orientation, etc. But in 2004, a groundbreaking economic study on race in the labor market found that people with white-sounding names were 50% more likely to get a call back from an HR professional. This study was the beginning of a sea-change in economics, where organizations slowly realized the economic need for, and importance of, DE&I. In effect, organizations began to slowly understand that there was an economic cost to not hiring the best candidates, and that focusing on DE&I increases profitability, productivity, and growth.This sea-change is represented across the globe. European countries were initially on the forefront of this movement, as evidenced by the 2003 emphasis in Norway to have gender equity represented on corporate boards within the country. The US is now moving even further in that direction. Last year, Nasdaq proposed new board diversity rules and disclosure guidance, including that listed companies should have at least one board member who identifies as a woman, as well as one board member who self-identifies as an underrepresented minority or LGBTQ+.As McManus pointed out, this trend is also represented across the legal industry. There is a continued expectation for more diversity, equity, and inclusion within organizations, law firms, and legal technology supply vendors. Clients want to see diversity, equity, and inclusion represented in the teams they work with on a daily basis. Additionally, the next generation of talented employees is also demanding an equitable environment in which to work. Thus, legal and eDiscovery employers should expect that going forward, they will need to track, measure, and demonstrate an inclusive, equitable, and diverse environment in order to attract and retain the best workers.As Rossell pointed out: “(DE&I) matters to the next generation. As talent becomes scarcer and the balance of power shifts away from employers to employees, [the next generation of workers] is going to demand not only a flexible workforce but a diverse and inclusive environment to work in.”As DE&I programs advance, eDiscovery and legal teams will see how diverse hiring contributes to greater innovation and success.AI and Its Role in the Legal IndustryRossell also provided a historical view of technology innovation and its effect on worldwide economies. She noted that artificial Intelligence (AI) technology is the next step in a 200-year-old process that began with the industrial revolution – when advances in machine automation allowed simple machines to perform manufacturing related processes, enabling humans to migrate towards more service-related work. This has now evolved into machines that can now perform some of the work in the service sector, thanks to advancements in AI technology.McManus noted that within the legal industry, lawyers (who are trained to be risk-averse) have traditionally been much slower to adopt this emerging technology. However, the legal industry is also quickly becoming submerged in “big data,” and AI is one of the most effective tools to combat the labor shortages and increased costs that exacerbate the problems caused by massive data volumes. Nowhere is this more evident than in the document review process performed during eDiscovery.“The industry still follows a traditional approach [to document review] with large groups of lawyers reviewing massive volumes of text and that approach is just untenable,” McManus said.The impracticality of that traditional approach is not only due to the increased volume and complexity of data, but also due to labor shortages and increased labor costs. Advancements in AI give newer legal technology tools the capability to help automate and expedite the document review process. This should lead to AI adoption at a much faster pace than we’ve traditionally seen in the legal industry, McManus noted.The Global Regulatory Landscape, Anti-Trust Activity, and What to Look for in the Coming YearsRossell also provided an insightful overview of the dynamic and shifting regulatory landscape from an economist’s perspective. Increased governmental regulation is raising costs in almost every industry and is one of the driving forces behind higher inflation rates. In the United States, the increase in government regulation may be due to the fact that the government’s governing functions have been slowly shifting from the legislative branch to the executive branch. In turn, this shift means that every four years, companies may deal with a complete shift in the regulatory landscape depending on which political party wins the presidential office. These abrupt swings make compliance very costly and put pressure on smaller organizations. Often the only companies that can survive this type of volatility are those big enough to support a department solely dedicated to compliance. Thus, in some ways, increased government regulation is driving the consolidation of companies.At the same time, we are seeing a shift in antitrust policy from an economics perspective. Whereas previously, anti-competition policy was centered around whether consolidation would harm consumers, we’re now seeing a shift to assessing a broader range of harm. Prior to this shift, a merger would be blocked if it would cause higher prices for consumers (i.e., if the merger would cause consumers harm by giving them less choices and therefore raise consumer prices). Now, mergers are blocked for a much broader range of issues that are not just centered solely around consumers, but around society as a whole. For example, a merger might now be blocked if it would be harmful to the environment, to workers, would cause a decline in future competition, etc. This more aggressive governmental regulation worldwide is expected to continue in the coming years. In short, expect anti-competition scrutiny to continue to be broad and aggressive, regardless of changes in political parties and offices.The Future of the Global Data Privacy LandscapeFinally, McManus provided a helpful overview of recent changes to the data privacy landscape, and what to expect in the 2022. Another area where government regulation is expected to continue to increase globally is around data privacy rights and protections for consumers. The EU’s GDPR legislation in 2018 paved the way for data privacy rights, providing a template for governments on how to regulate and protect consumer data privacy. Within a few years, California followed suit, as did a plethora of other governments around the world. This trend is only expected to continue as we move into an increasingly digital world.In the US in 2021 alone, two more states passed comprehensive GDPR-like laws (Virigina and Colorado), while at least 25 other states introduced or had data privacy laws somewhere within the state legislative consideration process. And the US federal government also looks to be increasingly active in this area – with the U.S. House Energy and Commerce Committee voting to give the Federal Trade Commission $1 billion to set up a data privacy bureau. Even China passed a GDPR-like law in 2021, the Personal Information Protection Law, which included not only the risk of huge fines for non-compliance, but also the risk of companies being black-listed by the Chinese government.This focus on data privacy regulation will certainly increase costs for businesses in the coming years, as companies work to stay compliant with a patchwork of global and local data privacy laws and regulations.ediscovery-reviewccpa, gdpr, review, ai-big-data, blog, ediscovery-review,ccpa; gdpr; review; ai-big-data; bloglighthouse

Legal teams approaching a Hart-Scott-Rodino (HSR) Second Request may hold false assumptions about what is and isn’t possible with document review. Often these appear as necessary evils—compromises in efficiency and precision are inevitable given the unique demands of Second Requests. But, in fact, these compromises are only necessary in the context of legacy technology and tools. Using more current tools, legal teams can transcend many of these compromises and do more with document review than they thought possible.Document review during an HSR Second Request is notoriously arduous. Legal teams must review potentially millions of documents in a very short timeframe, as well as negotiate with regulators about custodians and other parameters that could change the scope of the data under review.Up until recently, legal teams’ ability to meet these demands was limited by technology. It wasn’t possible to be precise and thorough while also being extremely quick. As a result, attorneys adopted certain conventions and concessions around the timing of review steps and how much risk to accept.Technology has evolved since then. For example, tools powered by advanced artificial intelligence (AI) utilize deep learning models and big data algorithms that make review much faster, more precise, and more resilient than legacy tools. However, legacy thinking around how to prepare for Second Requests remains. Many attorneys and teams remain beholden to the constraints imposed on them by tools of the past. New review tools enable new approaches and benefits, eliminating these constraints. Here’s a look at three of the most common myths surrounding document review during Second Requests and how they’re proven false by modern review tools.Myth 1: Privilege review must come after responsive reviewThe classic approach to reviewing documents during a Second Request is to start by creating a responsive set and then review that set for privileged documents. This takes time— an extremely precious commodity during a Second Request—but these steps are unavoidable with legacy tools. The linear nature of legacy review models requires responsive review to happen first because supporting privilege review over an entire dataset simply is not a feasible task over potentially millions of records. Tools leveraging advanced AI, however, are well suited to support scalable privilege analysis with big data. Rather than save privilege review for later, legal teams can conduct privilege review simultaneously with responsive review. This puts documents in front of human reviewers sooner and shaves invaluable hours off the timeline as a whole.Myth 2: Producing privileged documents to regulators is inevitableInadvertently disclosing privilege documents to federal agencies is so common the Federal Rules of Civil Procedure give parties some latitude to do so without penalty. Even so, the risk remains of inadvertent disclosure during a Second Request that will invite additional questions and scrutiny from regulators and undermine the deal.Although advanced AI tools cannot eliminate the possibility of inadvertent disclosure, these automated solutions can vastly reduce it. In one recent Second Request, a tool using advanced AI was able to identify and withhold 200,000 privilege documents that a legacy tool had failed to catch. This spared the client from costly exposure and clawbacks.Myth 3: There’s no time to know the details of what you’re producingWith massive datasets and very little time to review, legal teams get used to producing documents without fully knowing what’s in them. This can cause surprise and pain down the line when regulators ask for clarification about information the team isn’t prepared to address.With advances in technology, teams can gain more clarity using tools that identify key documents. These tools conduct powerful searches of both text and document attributes, using complex and dynamic search strings managed by linguistic experts. Out of a million or more documents, key document identification can surface the one or two thousand that speak precisely to attorneys’ priorities, efficiently helping counsel prepare for testimony and other proceedings.What’s your Second Request strategy?Second Requests will always be intense. Advancements in eDiscovery technology prove the limits of the past don’t apply today. With technology moving beyond legacy tools, it is time for teams to move beyond legacy thinking as well.For more detail about how advancements in technology help teams meet the demands of Second Requests, download our eBook.antitrust; ediscovery-review; ai-and-analyticsreview, hsr-second-requests, blog, antitrust, ediscovery-review, ai-and-analytics,review; hsr-second-requests; blogkamika brown

Last year, California passed legislation that alters civil discovery procedures and significantly impacts discovery for all litigants in state court. This change in the state court rules of civil procedure essentially makes it mandatory for the producing party to identify the specific discovery request to which each and every document is responsive. Many fear this new rule will exponentially increase the cost and burden of discovery requests. The good news is there’s a simple solution: use technology to easily automate the process. In this blog, I’ll discuss a brief overview of the rule, the potential impact, and how technology can save the day and provide an automated and cost-effective solution.The RuleBeginning on January 1, 2020, California’s Code of Civil Procedure § 2031.280 was amended by legislation S.B. 370 to make it a requirement that documents planned for production identify “the specific request number to which the documents respond.” Prior to this rule (and as is the case in the majority of jurisdictions both in federal and state courts), documents could either be produced as they are maintained in the usual course of business, or organized to correlate with the categories in the discovery demand. By mandating this new way of organizing and labeling documents, S.B. 370 marks the establishment of a major new requirement for document productions and impacts all pending and active cases that are subject to California’s Civil Discovery Act. Of note, the new rule is vague on the procedural front and fails to identify how exactly litigants should fulfill the requirements, leaving open questions that courts will likely need to address in the future.Potential ImpactThe rule change is weighted towards the goal of saving the requesting party time and streamlining reviews so that large quantities of documents aren’t received without any indication of which discovery request they relate to. Litigants are also concerned, however, that a heavy burden in terms of time and cost is created by S.B. 370 for producing parties. Imagine a case involving a large-scale ESI production with thousands upon thousands of documents where the producing party must go through and manually identify every document and exactly which request it is responsive to. The time it would take to manually organize a large production at this level would almost certainly greatly increase the length of the review due to the challenge that is involved with manually determining how each document correlates to a specific discovery demand. Ultimately, the biggest potential impact of S.B. 370 is higher litigation costs as a result of a lengthened review if a manual process is left in place.The SolutionWhen contemplating the bigger burden this new rule might place on producing parties, there’s also a unique opportunity that presents itself. With the use of technology, large reviews can be managed with an automated solution that would decrease the time from review to production and reduce costs. At a high level, the solution would entail:Identify the Issues - Identifying a comprehensive list of issues involved in the review.Map the Issues - Once the issues are understood, they would be mapped to a numbered list of specific discovery requests.Review and Tag - Armed with that organizational structure, the reviewers would conduct their document review and tag the documents by issue as per usual. At the completion of the review, the solution would automatically link the documents to each category based on the original map we created at the commencement of the review.Report Back - A report could also be generated to be provided with the final production set. That list could be produced in a sortable spreadsheet or it could be automated to connect to separate tags within the review database so it could be searched as contemplated. With S.B. 370 now in effect, it’s important to set up an automated process that will address the changes and potentially create a better organized and more cost-effective review. ediscovery-reviewediscovery-process, blog, ediscovery-review,ediscovery-process; bloglighthouse

self-service, spectra models are becoming increasingly more popular within the eDiscovery space. The ability to easily manage matters using in house teams, not only saves time and money, but it also allows companies to scale and maintain control in the ever-growing data landscape we live in today, without having to make the investment in infrastructure or additional headcount. It is no wonder so many firms and corporations are making the shift to a technology on-demand model and upgrading their internal processes.However, whether you are hoping to move away from a legacy platform or looking to upgrade your current self-service, spectra tool-kit, the process can seem intimidating and may take some convincing for those not completely on board. Below I outline some key steps to help you build a business case to propose to your teams and get the ball rolling when it comes to onboarding or upgrading your self-service, spectra practice.1. Assess the Interests of the Decision Makers – This is the first key step to getting started and will help you build your business case moving forward. To get started, list the current challenges your key decision makers are facing and whether they can be addressed with an upgraded self-service, spectra model. If folks are unsure, review the key benefits of modern self-service, spectra solutions and explore if any of them resonate.2. Outline the Goal – Once you understand the interests of your decision makers, the next step is to identify their key needs and requirements. For example, what matters most to your team? Is it accessibility, speed, data analytics, scalability, cost recovery, all-in-one tool, ease of use, low maintenance, controlled access, limited professional service hours, etc.? Define their top requirements and overall goals, and keep those top of mind while executing the next few steps.3. Do the Research – Next, dig into those challenges and key requirements and how an upgraded self-service, spectra model may meet those needs. Why will this model be of value to your team, and more specifically what are the top benefits and how do those overlap with the decision maker’s needs? Are there any client success stories that are relatable to your team’s situation? Stats?4. Develop the Pitch – Once you have conducted your research and have a solid list of key findings and benefits, outline them in a digestible manner. Think competitive matrices, tables, and PowerPoint. Feel free to leverage this Excel or PDF self-service, spectra selection matrix template. Lay out the key reasons why an upgrade makes sense and how it will meet the needs of your team.5. Present the Findings – Prior to presenting, get a meeting invite on the books with details and expectations (i.e. looking for decision maker’s feedback and preferences). It is also a good idea to preview the findings with your leader or a trusted colleague who can weigh in and provide ideas to enhance your presentation. Present your findings, any key client success stories you uncovered, as well as the benefits that matter most to your team.6. Continue the Communication – After the presentation, be sure to follow up and address any concerns or questions that came up in the meeting. If needed, set another meeting to hone in on some of those questions. Ask for feedback and continue the conversation.Building a business case to upgrade your self-service, spectra practices can require upfront research and tough conversations, but this simple six-step guide should ease that process. To discuss these steps further or for assistance developing your business case, feel free to reach out to me at bthompson@lighthouseglobal.com.ediscovery-reviewcloud, self-service, spectra, blog, ediscovery-review,cloud; self-service, spectra; blogbrooks thompson

It’s no secret that big data can mean big challenges in the eDiscovery world. Data volumes and sources are exploding year after year, in part due to a global shift to digital forms of communication in working environments (think emails, chat messages, and cloud-based collaboration tools vs. phone calls, in-person meetings, and paper memorandums, etc.) as well as the rise of the Cloud (which provides cheaper, more flexible, and virtually limitless data storage capabilities).This means that with every new litigation or investigation requiring discovery, counsel must collect massive amounts of potentially relevant digital evidence, host it, process it, identify the relevant information within it (as well as pinpoint any sensitive or protected information within that relevant data) and then produce that relevant data to the opposing side. Traditionally, this process then starts all over again with the next litigation – often beginning back at square one in a vacuum by collecting the exact same data for the new matter, without any of the insights or attorney work product gained from the previous matter.This endless cycle is not sustainable as data volumes continue to grow exponentially. Fortunately, just as advances in technology have led to increasing data volumes, advances in artificial intelligence (AI) technology can help tackle big data challenges. Newer analytics technology can now use multiple algorithms to analyze millions of data points across an organization’s entire legal portfolio (including metadata, text, past attorney work product, etc.) and provide counsel with insights that can improve efficiency and curb the endless cycle of re-inventing the wheel on each new matter. In this post, I’ll outline the four main challenges big data can pose in an eDiscovery environment (also called “The Four Vs”) and explain how cutting-edge big data analytics tools can help tackle them.The “Four Vs” of Big Data Challenges in eDiscovery 1. The volume, or scale of dataAs noted above, a primary challenge in matters involving discovery is the sheer amount of data generated by employees and organizations as a whole. For reference, most companies in the U.S. currently have at least 100 terabytes of data stored, and it is estimated that by 2025, worldwide data will grow 61 percent to 175 zettabytes.As organizations and individuals create more data, data volumes for even routine or small eDiscovery matters are exploding in correlation. Unfortunately, court discovery deadlines and opposing counsel production expectations rarely adjust to accommodate this ever-growing surge in data. This can put organizations and outside counsel in an impossible position if they don’t have a defensible and efficient method to cull irrelevant data and/or accurately identify important categories of data within large, complex data sets. Being forced to manually review vast amounts of information within an unrealistic time period can quickly become a pressure cooker for critical mistakes – where review teams miss important information within a dataset and thereby either produce damaging or sensitive information to the opposing side (e.g., attorney-client privilege, protected health information, trade secrets, non-relevant information, etc.) or in the inverse, fail to find and produce requested relevant information.To overcome this challenge, counsel (both in-house and outside counsel) need better ways to retain and analyze data – which is exactly where newer AI-enabled analytics technology (which can better manage large volumes of data) can help. The AI-based analytics technology being built right now is developed for scale, meaning new technology can handle large caseloads, easily add data, and create feedback loops that run in real time. Each document that is reviewed feeds into the algorithm to make the analysis even more precise moving forward. This differs from older analytics platforms, which were not engineered to meet the challenges of data volumes today – resulting in review delays or worse, inaccurate output that leads to critical mistakes.2. The variety, or different forms of dataIn addition to the volume of data increasing today, the diversity of data sources is also increasing. This also presents significant challenges as technologists and attorneys continually work to learn how to process, search, and produce newer and increasingly complicated cloud-based data sources. The good news is that advanced analytics platforms can also help manage new data types in an efficient and cost-effective manner. Some newer AI-based analytics platforms can provide a holistic view of an organization’s entire legal data portfolio and identify broad trends and insights – inclusive of every variety of data present within it. These insights can help reduce cost and risk and sometimes enable organizations to upgrade their entire eDiscovery program. A holistic view of organizational data can also be helpful for outside counsel because it also enables better and more strategic legal decisions for individual matters and investigations.3. The velocity, or the speed of dataWithin eDiscovery, the velocity of data not only refers to the speed at which new data is generated, but also the speed at which data can be processed and analyzed. With smaller data volumes, it was manageable to put all collected data into a database and analyze it later. However, as data volumes increase, this method is expensive, time consuming, and may lead to errors and data gaps. Once again, a big data analytics product can help overcome this challenge because it is capable of rapidly processing and analyzing iterative volumes of collected data on an ongoing basis. By processing data into a big data analytics platform at the outset of a matter, counsel can quickly gain insights into that data, identifying relevant information and potential data gaps much earlier in the processes. In turn, this can mean lower data hosting costs as objectively non-responsive data can be jettisoned prior to data hosting. The ability of big data analytics platforms to support the velocity of data change also enables counsel and reviewers to be more agile and evolve alongside the constantly changing landscape of the discovery itself (e.g., changes in scope, custodians, responsive criteria, court deadlines).4. The veracity, or uncertainty of dataWithin the eDiscovery realm, the veracity of data refers to the quality of the data (i.e., whether the data that a party collects, processes, and produces is accurate and defensible and will satisfy a discovery request or subpoena). The veracity of the data produced to the opposing side in a litigation or investigation is therefore of the utmost importance, which is why data quality control steps are key at every discovery stage. At the preservation and collection stages, counsel must verify which custodians and data sources may have relevant information. Once that data is collected and processed, the data must then be checked again for accuracy to ensure that the collection and processing were performed correctly and there is no missing data. Then, as data is culled, reviewed, and prepared for production, multiple quality control steps must take place to ensure that the data slated to be produced is relevant to the discovery request and categorized correctly with all sensitive information appropriately identified and handled. As data volumes grow, ensuring the veracity of data only becomes more daunting.Thankfully, big data analytics technology can also help safeguard the veracity of data. Cutting-edge AI technology can provide a big-picture view of an organization’s entire legal portfolio, enabling counsel to see which custodians and data sources contain data that is consistently produced as relevant (or, in the alternative, has never been produced as relevant) across all matters. It can also help identify missing data by providing counsel with a holistic view of what was collected in past matters from data sources. AI-based analytics tools can also help ensure data veracity on the review side within a single matter by identifying the inevitable inconsistencies that happen when humans review and categorize documents within large volumes of data (i.e., one reviewer may categorize a document differently than another reviewer who reviewed an identical or very similar document, leading to inconsistent work product). Newer analytics technology can more efficiently and accurately identify those inconsistencies during the review process so that they can be remedied early on before they cause problems. Big Data Analytics-Based MethodologiesAs shown above, AI-based big data analytics platforms can help counsel manage growing data volumes in eDiscovery.For a more in-depth look at how a cutting-edge analytics platform and big data methodology can be applied to every step of the eDiscovery process in a real-world environment, please see Lighthouse’s white paper titled “The Challenge with Big Data.” And, if you are interested in this topic or would like to talk about big data and analytics, feel free to reach out to me at KSobylak@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewcloud, analytics, ai-big-data, ediscovery-process, prism, blog, ai-and-analytics, ediscovery-reviewcloud; analytics; ai-big-data; ediscovery-process; prism; blogkarl sobylak

The current state of eDiscovery is complex, inefficient, and cost prohibitive as data types and volumes continue to explode without bounds. Organizations of all sizes are bogged down in enormous amounts of unresponsive and duplicative electronically stored information (ESI) that still make it to the review stage, persistently the most expensive phase of eDiscovery.Data is at the center of this conundrum and it presents itself in a number of forms including:Scale of Data - In the era of big data, the volume, or amount of data generated, is a significant issue for large-scale eDiscovery cases. By 2025, IDC predicts that 49 percent of the world’s stored data will reside in public cloud environments and worldwide data will grow 61 percent to 175 zettabytes.Different Forms of Data - While the volume of ESI is dramatically expanding, the diversity and variety are also greatly increasing, and a big piece of the challenge involved with managing big data is the varying kinds of data the world is now generating. Gone are the days in eDiscovery where the biggest challenge was processing and reviewing structured, computer-based data like email, spreadsheets, and documents.Analysis of Data - Contending with large amounts of data creates another significant issue around the velocity or speed of the data that’s generated, as well as the rate at which that data is processed for collection and analysis. The old approach is to put everything into a database and try to analyze it later. But, in the era of big data, the old ways are expensive and time-consuming, and the much smarter method is to analyze in real time as the data is generated.Uncertainty of Data - Of course, with data, whether it’s big or small, it must be accurate. If you’re regularly collecting, processing, and generally amassing large amounts of data, none of it will matter if your data is unreliable or untrustworthy. The quality of data to be analyzed must first be accurate and untainted.When you combine all of these aspects of data, it is clear that eDiscovery is actually a big data and analytics challenge!While big data and analytics has been historically considered too complex and elaborate, the good news is that massive progress has been made in these fields over the past decade. Access to the right people, process, and technology in the form of packaged platforms is more accessible than ever.Effective utilization of a robust and intelligent big data and analytics platforms enable organizations to revamp their inefficient and non-repeatable eDiscovery workflows by intelligently learning from past cases. A powerful big data and analytics tool utilizes artificial intelligence (AI) and machine learning to create customized data solutions by harvesting data from all of a client’s cases and ultimately creating a master knowledge base in one big data and analytics environment.In particular, the most effective big data and analytical technology solution should provide:Comprehensive Analysis – The ability to integrate disparate data sources into a single holistic view. This view gives you actionable insights, leading to better decision making and more favorable case outcomes.Insightful Access – Overall and detailed visibility into your data landscape in a manner that empowers your legal team to make data-driven decisions.Intelligent Learnings – The ability to learn as you go through a powerful analytics and machine learning platform that enables you to make sense of vast amounts of data on demand.One of the biggest mistakes organizations make in eDiscovery is forgoing big data and analytics to drive greater efficiency and cost savings. Most organizations hold enormous amounts of untapped knowledge currently locked away in archived or inactive matters. With big data and analytics platforms more accessible than ever, the opportunity to learn from the past to optimize the future is paramount.If you are interested in this topic or just love to talk about big data and analytics, feel free to reach out to me at KSobylak@lighthouseglobal.com.ai-and-analytics; ediscovery-reviewai-big-data, blog, ai-and-analytics, ediscovery-reviewai-big-data; blogkarl sobylak

It’s an exciting time in the world of legal tech as SaaS eDiscovery solutions, and cloud computing in general, represent an enormous amount of potential with nearly unlimited capacity of storage, power, and scalability, whether you’re handling small or very large matters. Once seen as something only big firms need to deal with for large cases, we’ve seen electronic communication in the workplace (like email and chat) become the norm and consequently eDiscovery become a typical domain for law firms of all shapes and sizes. It makes perfect sense that the proliferation of an easy-to-use, cost-effective solution is the future for an industry right on the cusp of its next iteration.So you’re ready to embrace this next era of eDiscovery and you’ve decided to adopt a SaaS, self-service, spectra tool within your firm? In my previous blog, I outlined three top reasons why SaaS makes the most sense for law firms in the age of cloud storage, especially as new and improved self-service, spectra tools incorporate the latest technology, are easy to use, and have significantly improved the efficiency of the typically arduous and expensive on-prem eDiscovery process.But transitioning even some of your firm’s in-house eDiscovery process to a SaaS solution requires careful thought around the complexities involved with security, solution support, and business continuity. To make the transition to SaaS as smooth as possible, it’s important to tailor your solution to your specific environment and create an implementation plan that will set you up for success. Here are a few suggestions for best practices to consider when you’re ready to embrace the self-service, spectra, SaaS eDiscovery revolution and leave your cumbersome on-prem environment behind.Eliminate your on-prem applications and infrastructure. Many firms have a patchwork of on-prem tools that they use for different phases of their eDiscovery workflow. A great starting point to eradicating the expense, headache, and risk that comes with maintaining your own infrastructure is to get rid of those old on-prem applications altogether and start fresh with a SaaS tool that will handle your entire workflow. That means choosing one comprehensive tool that allows you to create, upload, and process matters while also enabling you to manage your users across matters and locations from a single place. You’ll not only eliminate administrative headaches, you’ll no longer have to worry about managing data and will be free to concentrate on analyzing data while your SaaS solution provider takes on the security and infrastructure management for you.Leverage best-of-breed tools. A common problem for consumers of on-prem eDiscovery software has been needing to pull together multiple technologies to process, review, perform analytics, and produce data. While you’ve been working with that complicated patchwork of tools you’ve licensed and tried to maintain within your own IT environment, new versions of best-of-breed tools have evolved for everything from processing to analytics to review and production. Now that you’ve chosen one SaaS tool that can handle your full eDiscovery workflow, your new tool should provide you with access to the most updated and advanced tools across the EDRM without any maintenance or upgrades ever needing to be managed on your end.Ensure your solution is supported. Once you’re on board with a streamlined eDiscovery workflow with no infrastructure risks or administrative headaches and access to the most modern and best available eDiscovery tools, what happens when a matter becomes too large or unwieldy or you simply need access to a more traditional full-service support model? In this case, make sure you’re set up with a SaaS tool that is supported by a solution provider who can easily transition you from that self-service, spectra, on-demand eDiscovery model to one where they can take over when you need them to. In addition, speed to implementation is something to consider. While on-prem systems can take months to actually install and implement, a self-service, spectra, SaaS tool can literally be up and ready to use within days.Now that you’ve made the smart decision to modernize your eDiscovery program and implement a self-service, spectra, SaaS solution, it’s time to use these best practices to eliminate your expensive and risky infrastructure, streamline your workflow, adopt the most advanced best-in-breed tools, and benefit from a self-service, spectra tool that’s also backed with the peace of mind of full support from your solution provider. ediscovery-review; ai-and-analyticscloud, self-service, spectra, blog, ediscovery-review, ai-and-analyticscloud; self-service, spectra; bloglighthouse

Standardizing your eDiscovery program can be a huge benefit to you and your team. With a well-rounded program, you are able to pressure test and layer in repeatable and trackable processes at each stage of the EDRM. This will result in a lower overall cost of eDiscovery and the ability to more accurately forecast spend from matter to matter. Your program will reduce risk, and increase quality, efficiency, and consistency. You will also have the advantage of program-wide metrics and analysis, leading to knowledge that will empower you to make better and more informed litigation and investigation decisions early on, which in turn leads to better outcomes and greater defensibility. Finally, with your program-wide data tracking you will be able to showcase true ROI and other key metrics. It sounds pretty good, right? So, why doesn’t everyone standardize their eDiscovery program? It can be a challenge. There are several hurdles that one may face when trying to socialize and drive the adoption of their program. For example, lack of alignment across key stakeholders and the challenges of trying to build a program while also managing the pressures of ongoing litigation deadlines. You may also have to invest more time and potentially more cost upfront, which can be a resourcing challenge, and you may have to redefine efficiency across multiple teams. Managing expectations across key stakeholders is critical to building a successful program. Change doesn’t happen overnight.How do you go about overcoming these challenges and standardizing your program? I’ve summarized some tips and best practices below for socializing, implementing, and getting your eDiscovery program to be accepted as the standard both within your organization and beyond.Getting StartedTo begin, build one thing at a time. It is important not to bite off more than you can chew. Start with one project, implement it, and carefully review the results. If it is successful, drive adoption internally, and once it is adopted you can get started on the next project or piece of the program. Be sure all of your key stakeholders are involved early on and set up weekly or even monthly strategy sessions with these stakeholders to ensure that everyone has a seat at the table and a voice in program development decisions. Finally, documentation is your single source of truth. Be sure to think about what you are documenting, where you are storing it, when it should be evaluated for updates, and how it will be circulated after these updates are made. More on driving a successful eDiscovery project can be found in this article, Staying on Pointe: Key Lessons eDiscovery Professionals can Learn from Ballet.Ensuring the Right AudienceAs I mentioned above, you need to be sure to involve all key stakeholders when driving the standardization of your eDiscovery program, but how do you make sure you have the right audience? It is different for everyone and will depend on your organization. Typically, I would recommend that you involve your legal operations and finance teams, as well as any other teams with eDiscovery stakeholders. Once you have these folks identified, set up that recurring strategy meeting.Showing ROIWhen it comes to showing ROI you want to be sure to pick what will make an impact within your company. Whether that be risk reduction, cost reduction, efficiency gains, or something else, you want to focus on what matters at your organization. This is where the documentation I mentioned above comes into play. Be sure you are tracking the metrics and results you would like to report on and format them in graphs, charts, and high-level stats that your key stakeholders can take away and share with their teams. Lean on your providers to help you pull metrics and come up with creative ways to display ROI across your program. It is also important to note that your ROI focus may shift over time, so be sure to remain flexible and check-in with leaders on a bi-annual or annual cadence.Socializing & Driving AdoptionSo, you know how to get started, who to involve, and how to show ROI, but how do you socialize and drive adoption? This is the hardest part and will require flexibility. It is important not to design and drop. You have to continue to reiterate the program and processes consistently. Document your processes, track your results, and make sure you build in a regular feedback loop. Ensure you have support from the right people. This can include your internal teams, outside counsel, vendor(s), etc., and can vary depending on your organization. Be open to feedback and revisions as they come along, document those updates, and share them out.To summarize, when looking to standardize and socialize your eDiscovery program, remember to:involve the right folks early on;build one thing at a time;document the processes;show meaningful ROI; andbe open to feedback - a successful program evolves!To discuss this topic further, please feel free to continue the discussion by emailing me at SBarsky-Harlan@lighthouseglobal.com.ediscovery-review; legal-operationsediscovery-process, blog, ediscovery-review, legal-operationsediscovery-process; blogsarah barsky harlan

Identifying attorney-client privilege is one of the most costly and time-consuming processes in eDiscovery. Since the dawn of the workplace email, responding to discovery requests has had legal teams spending countless hours painstakingly searching through millions of documents to pinpoint attorney-client and other privileged information in order to protect it from production to opposing parties. As technology has improved, legal professionals have gained more tools to help in this process, but inevitably, it still often entails costly human review of massive amounts of documents.What if there was a better way? Recently, I had the opportunity to gather a panel of eDiscovery experts to discuss how advances in AI and analytics technology now allow attorneys to identify privilege more efficiently and accurately than previously possible. Below, I have summarized our discussion and outlined how legal teams can leverage advanced AI technology to reinvent the model for detecting attorney-client privilege.Current Methods of Privilege Identification Result in Over IdentificationCurrently, the search for privileged information includes a hodgepodge of different technology and workflows. Unfortunately, none of them are a magic bullet and all have their own drawbacks. Some of these methods include:Privilege Search Terms: The foundational block of most privilege reviews involves using common privilege search terms (“legal,” “attorney,” etc.) and known attorney names to identify documents that may be privileged, and then having a review team painstakingly re-review those documents to see if they do, in fact, contain privileged information.‍Complex Queries or Scripts: This method builds on the search term method by weighting the potential privilege document population into ‘tiers’ for prioritized privilege review. It sometimes uses search term frequency to weigh the perceived risk that a document is privileged.‍Technology Assisted Review (TAR): The latest iteration of privilege identification methodologies involves using the TAR process to try to further rank potential privilege populations for prioritized review, allowing legal teams to cut off review once the statistical likelihood of a document containing privilege information reaches a certain percentage.Even applied together, all these methodologies are only just slightly more accurate than a basic privilege search term application. TAR, for example, may flag 1 out of every 4 documents as privilege, instead of the 1 out of every 5 typically identified by common privilege search term screens. This result means that review teams are still forced to re-review massive amounts of documents for privilege.The current methods tend to over-identify privilege for two very important reasons: (1) they rely on a “bag of words” approach to privilege classification, which removes all context from the communication; (2) they cannot leverage non-text document features, like metadata, to evaluate patterns within the documents that often provide key contextual insights indicating a privileged communication.How Can Advances in AI Technology Improve Privilege Identification MethodsAdvances in AI technology over the last two years can now make privilege classification more effective in a few different ways:Leveraging Past Work Product: Newer technology can pull in and analyze the privilege coding that was applied on previous reviews, without disrupting the current review process. This helps reduce the amount of attorney review needed from the start, as the analytics technology can use this past work product rather than training a model from scratch based on review work in the current matter. Often companies have tens or even hundreds of thousands of prior privilege calls sitting in inactive or archived databases that can be leveraged to train a privilege model. This approach additionally allows legal teams to immediately eliminate documents that were identified as privileged in previous reviews.Analyzing More Than Text: Newer technology is also more effective because it now can analyze more than just the simple text of a document. It can also analyze patterns in metadata and other properties of documents, like participants, participant accounts, and domain names. For example, documents with a large number of participants are much less likely to contain information protected by attorney-client privilege, and newer technology can immediately de-prioritize these documents as needing privilege review.Taking Context into Account: Newer technology also has the ability to perform a more complicated analysis of text through algorithms that can better assess the context of a document. For example, Natural Language Processing (NLP) can much more effectively understand context within documents than methods that focus more on simple term frequency. Analyzing for context is critical in identifying privilege, particularly when an attorney may just be generally discussing business issues vs. when an attorney is specifically providing legal advice.Benefits of Leveraging Advances in AI and Analytics in Privilege ReviewsLeveraging the advances in AI outlined above to identify privilege means that legal teams will have more confidence in the accuracy of their privilege screening and review process. This technology also makes it much easier to assemble privilege logs and apply privilege redactions, not only to increase efficiency and accuracy, but also because of the ability to better analyze metadata and context. This in turn helps with privilege log document descriptions and justifications and ensuring consistency. But, by far the biggest gain, is the ability to significantly reduce costly and time-intensive manual review and re-review required by legal teams using older search terms and TAR methodologies.ConclusionLeveraging advances in AI and analytics technology enables review teams to identify privileged information more accurately and efficiently. This in turn allows for a more consistent work product, more efficient reviews, and ultimately, lower eDiscovery costs.If you’re interested in learning more about AI and analytics advancements, check out my other articles on how this technology can also help detect personal information within large datasets, as well as how to build a business case for AI and win over AI naysayers within your organization.To discuss this topic more or to learn how we can help you make an apples-to-apples comparison, feel free to reach out to me at RHellewell@lighthouseglobal.com.ai-and-analytics; chat-and-collaboration-data; ediscovery-reviewprivilege, analytics, ai-big-data, blog, ai-and-analytics, chat-and-collaboration-data, ediscovery-review,privilege; analytics; ai-big-data; bloglighthouse

Legal department automation may be top of mind for you like several other legal operations professionals, however, you might be dependent on IT or engineering resources to be able to execute. Or perhaps you are struggling with change management and not able to implement something new. You are not alone. These were the top two blockers to building out an efficient process within legal departments as shared by recent CLOC conference attendees. The good news is that off-the-shelf technologies have advanced to the point where you may not need any time from those resources and may be able to manage automation without needing to change user behavior. With “no code” automation, you can execute end-to-end automation for your legal operations department, yourself!What is “No Code” Automation?As recently highlighted in Forbes magazine, “no-code platforms feature prebuilt drag-and-drop activities and tasks that facilitate integration at the business user level.” This is not “low code” automation that has been around for decades. Low code refers to using existing code, whether from open source or from other internal development, to lower the need to create new code. Low code allows you to build faster but still requires the knowledge of code. In “no code,” however, you do not need to have an understanding of coding. What this really means is that no code platforms are so user-friendly that even a lawyer, or legal operations professional, can create automated actions…I know because I am a lawyer that has successfully done this!But, How Does this Apply in Legal Operations?The short answer is that it lets you, the legal operations professional, automate workflows with little external help. There are some legal departments already taking advantage of this technology. At a recent CLOC conference, Google shared how they had leveraged “no code” automation to remove the change management process for ethics and compliance in the code of conduct, conflict of interest, and anti-bribery and corruption areas. With respect to outside counsel management, Google was similarly able to remove IT/engineering dependencies for conflict waiver approvals, outside counsel engagements, and matter creation. For more details, watch Google describe their no-code automation use cases.Google’s workflow automation is impressive and more mature than those of us who are just starting, so I wanted to share a simple example. A commonplace challenge for smaller legal teams is to manage tasks – ensuring all legal requests are captured and assigned to someone on the legal team. Many teams are dealing with dozens, or hundreds, of emails and it can be cumbersome to look through those to determine who is working on what. Inevitably some of those requests get missed. It is also challenging to then later report on legal requests – e.g., what types of requests the legal team receives daily, how long they take to resolve, and how many requests each person can work on. A “no code” platform can help. For example, you can connect your email to a shared Excel spreadsheet that captures all legal tasks. You would do this by creating a process that has the tool log each email sent to a certain address (e.g. legal@insertconame.com) on an Excel spreadsheet in a shared location (e.g. LegalTasks.xls). You would “map” parts of the email to columns in the spreadsheet. For example, you would want to capture the sender, the date, the time, the subject, and the body. You can even ask users who are sending requests into that email to put the type of request in the subject line. Your legal team can then check the shared spreadsheet daily and “check out” tasks by putting their initials in another column. Once complete, they would also mark that on the spreadsheet. Capturing all this information will allow you to see who is working on what, ensure that all requests are being worked on, and use pivot reporting on all legal tasks later on. Although this is a really simple use case with basic tools, it is also one that takes only a few minutes to set up and can measurably improve organization among legal team members.You can use “no code” automation in most areas of legal operations department automation. Some of the most common things to automate with “no code” are as follows:Legal ApprovalsDocument GenerationsEvidence CollectionTracking of Policy AcceptanceMany “no code” companies work with legal departments, so they may have experience with legal operations use cases. Be sure to ask how they have seen their technologies deployed in other legal departments.Can I Really Do This Without Other Departments?About 90% of the work can be done by you or your team, and in some cases, even 100%. However, sometimes connecting the tools or even installing the software has to be done by your IT and development teams. This is particularly true if you are connecting to proprietary software or have a complex infrastructure. This 10% of work required by these teams, however, is much smaller than if you were asking for those resources to create the automations from scratch. In addition, you often do not have to change user behavior so change management is removed as a blocker.I encourage you to explore using “no code” automation in your legal department. Once you start, you’ll be glad you tried. I would be excited to hear your experiences with “no code” in legal operations. If you are using it, drop me a line at djones@lighthouseglobal.com and tell me how.legal-operations; ediscovery-reviewediscovery-process, legal-ops, blog, legal-operations, ediscovery-reviewediscovery-process; legal-ops; bloglighthouse

Among data challenges that businesses and their law firms face, those surrounding mergers and acquisitions are arguably some of the most daunting. Fast-paced and demanding, the high-stakes M&A process is like an amped-up litigation and investigation combined, with specific M&A data requirements, massive document productions, fact-finding imperatives, and more.To add a bit of drama, inflationary pressure and fears of a recession could cool M&A activity, while the impacts of the pandemic continue to make regulatory reaction to the M&A landscape unpredictable, especially as to whether an HSR Second Request will be in the offing. If there is Second Request, document requirements ramp up and so does heightened scrutiny from regulatory agencies, especially in light of the 2021 Executive Order on Promoting Competition in the American Economy. “Providing heightened scrutiny to a broader range of relevant market realities is core to fulfilling our statutory obligations under the law.” – FTC, 2021Know as much as you can, as soon as you canIn a Second Request (as with any legal matter), the more you know and the sooner you know it, the better. Since technology assisted review (TAR), continuous active learning (CAL), and other eDiscovery technology has largely usurped a linear responsive review process, there is often less need for attorneys to review the majority of the documents that get produced to the government. This is good news for attorneys, who are faced with ever-growing data volumes that would be nearly impossible to tackle using a linear document review process, while still meeting the tight substantial compliance timeframe in a typical Second Request. However, less human review during the eDiscovery process elevates the need for counsel to find a way to uncover key information within the documents for fact development, witness kits, or expert support.From a data standpoint, what fact-finding can be done early using human expertise, technology, and a specific search workflow? The sophisticated analytics tools available today make any number of assessments possible, even before data is collected. Basic data characteristics gleaned from metadata can reveal important information: email domains, recipients, BCCs, timestamps—such metadata is fodder for data analytics tools that can reveal custodians, relationships, timelines, communications patterns and more, all necessary information in regulatory matters. Companies that have found a way to have previously-assessed characteristics live with a document (think privilege, PII, confidentiality status) are really ahead of the game.Let’s also not forget that evidence of anti-competitive behavior is really what Second Requests are all about. Although there are plenty of market facts and figures to be scrutinized, communications among people who are knowledgeable about the proposed deal could tell an “interesting” story. Common words and phrases casually bandied about (“dominant player,” “sticky customers”) can be laden with meaning to regulators or attorneys, throwing up red flags for further investigation. Company data stores can thus either be a gold mine or a land mine—and it helps counsel tremendously if they have the information on hand to prepare for either circumstance. Finding key documents: a surgical strike, not a data dumpIdentifying key information requires a precise approach and assessment —it’s not something that can be accomplished with a keyword list created during a brainstorming session. Keywords can’t help much if you don’t know exactly what you’re looking for. Rather, finding key documents today can be an elevated process—one that is technology-enabled and executed by a nimble team that can leverage linguistic expertise, proven search algorithms and processes, and proprietary technology to quickly pinpoint and deliver a highly-curated set of documents on target topics. As key information is uncovered, further fact-finding can be curtailed or expanded. A team can adapt to any change in priorities, custodians, subjects, and/or time frames as a regulator changes the focus of the review. This reduces the amount of time counsel must spend going through documents, keeping costs in check, and providing the best ROI.Between the initial filing and receipt of a Second Request, especially when there is little doubt that the Second Request will be issued, a team executing key document identification can help kick off the fact-finding and development process with whatever data is available—before any responsiveness review has even begun.And even when no Second Request is issued, a team of experts executing key document identification can play a significant role. In support of an initial filing, they can help identify 4(c) and 4(d) documents that are required as part of the disclosure and get the best instance or latest version of important documents. This is especially helpful in situations where executives or others involved in the deal have massive data populations and don’t know where the relevant documents are. ConclusionIdentifying key documents is a critical part of a Second Request. If client and counsel are well-prepared—armed with the ability to leverage expertise and advanced technology to find key documents from the get-go—the most challenging hurdles can often be overcome, enabling timely compliance, and avoiding potential complications that could delay resolution—or even kill the deal. antitrust; ediscovery-reviewantitrust, ediscovery-reviewhsr-second-requests; bloglighthouse

A Second Request for a Hart-Scott-Rodino (HSR) filing thrusts companies and their counsel into a high-stakes race against time, complicated by massive data volumes and strict requirements. Policy and enforcement shifts by the Federal Trade Commission (FTC) and Department of Justice Antitrust Division (DOJ), brought on by a change in presidential administrations, complicate the landscape even further.In early 2022, Lighthouse analyzed the data and recent history of Second Requests in our whitepaper, the 2021 Second Request Trends Report, to help predict activity this year and beyond.Now, as we approach the halfway point of the Biden administration’s inaugural term, it seems a pertinent time to check in on the agencies’ attitudes and actions thus far, and what they mean for mergers and acquisitions — both today and in the future. To grasp the shifts in HSR Second Requests over the past two years, Lighthouse's Bill Mariano interviewed Corey Roush, a partner at Akin Gump who leads their antitrust and competition practice, and is head of their FTC-facing consumer protection practice. Below is an excerpt from their conversation.The Biden administration has now had more than a year and a half to shape its approach to mergers and acquisitions. How do you view the landscape at this point?I see outward signs of moderate hostility towards mergers that have created general uncertainty. This owes mostly to statements by leadership at both agencies rather than unexpected actions. For the most part, we are seeing Second Requests issued when one would traditionally expect them, and we are also seeing some high-profile public transactions like Elon Musk/Twitter and PMI/Swedish Match avoiding Second Requests.What have regulatory agencies done to create this atmosphere?A handful of things, from making specific policy changes to expressing general disdain for consolidation. The discourse coming from regulators is guided largely by a July 2021 Executive Order from President Biden. Inspired by that order, FTC Chair Lina Khan told Congress that “significant consolidation has undermined open and competitive markets” so it’s her agency’s responsibility “to redouble [its] commitment to policing mergers.” That attitude was echoed by Assistant Attorney General Jonathan Kanter, head of the Antitrust Division at DOJ, who said mergers “can harm downstream consumers and upstream workers at the same time that they foster coordination or exclusion in adjacent markets. Everyone loses, except extractive powerful firms in the middle.”Disdain for consolidation, at least among the largest companies, is an increasingly bipartisan posture, by the way. Last spring Senator Josh Hawley (R-Mo.) introduced the Trust-Busting for the Twenty-First Century Act, complaining that a small group of “woke mega-corporations control the products Americans can buy, the information Americans can receive” and so on. The legislation would help regulators “crack down on mergers and acquisitions by monopoly companies” and even “pursue the breakup of dominant, anticompetitive firms.”There’s the hostility you mentioned. What about enforcement? How are they following through on this rhetoric?Overall, by expecting companies to accommodate the agencies. You see cases where companies agree to delay consummation until three or four months after complying with a Second Request, so that agencies have more time to review. And even when companies agree to delay consummation under a timing agreement, the agencies may ask for even more time. Last year, 7-Eleven was three days away from closing an acquisition when the FTC asked for more time — and this was after the company had already given the Commission more time on four separate occasions. The company was able to close the deal as planned and without a Commission vote because it had already negotiated a consent decree approved by the FTC staff. Two Commissioners responded with a public threat stating, “The parties have closed their transaction at their own risk. The Commission will continue to investigate to determine an appropriate path forward to address the anticompetitive harm and will also continue to work with State Attorneys General.” After all that, a “new” consent order was issued that was almost identical to the one that the company had previously agreed to and was approved by the Commission on a 4-0 vote two months later.It seems like “close at your own risk” is becoming a trend now?It is. The FTC has been issuing letters since the fall of 2021 warning parties whose regulatory review periods had expired or were about to expire that the agency was continuing to investigate the transaction, so parties who decided to close on their planned date would do so at their own risk. By early 2022, the DOJ joined the fray, issuing at least one warning letter that I’m aware of. So far, though, it appears to be a red herring. First, parties have always closed with some risk of a post-closing challenge. For instance, the FTC is currently challenging Facebook’s acquisition of WhatsApp and Instagram—deals that were consummated eight and ten years ago, respectively. Second, in the current landscape, companies have been closing despite receiving the letters, and we haven’t seen any efforts to unwind those deals. Nor have we seen many investigations actually continue. What other policy changes have altered the landscape for HSR and Second Requests?The big one in my mind affects prior approval. In July of 2021, the FTC — by a 3-2 party-line vote — adopted a new policy that requires “buyers of divested assets in Commission merger consent orders to agree to a prior approval for any future sale of the assets they acquire in divestiture orders.” This rescinds a nearly 30-year-old policy and creates real complications in the divestiture process. To state the obvious, an asset is less attractive if it comes with a restriction on its sale and a requirement that the divestiture buyer sign a consent decree with the FTC. We now see these agreements in consent orders regularly. That said, we have also seen at least one consent order that did not require the divestiture buyer to sign on. What distinguished that case from the others is unclear.What does this all mean going forward? What should parties expect from regulators?Longer reviews, with unpredictable engagement. Some deals that do not present clear competition problems are taking longer than one might traditionally expect. At the same time, we have avoided Second Requests even though, at first glance, there were competitive overlaps and/or vertical relationships. In those cases, along with competitive analysis proving the transaction wasn’t troublesome, our early engagement with the agencies appeared to be key. The uncertainty applies mostly to certain high-profile, high-scrutiny areas like tech, pharma, and agriculture. Deals outside of those areas appear to be more predictable and consistent with past scrutiny. So, will 2023 be more of the same?Most likely. Legislation like the American Innovation and Choice Online Act and Open App Markets Act have bipartisan support. Alvaro Bedoya was confirmed as the third Democrat Commissioner in May. And the antitrust agencies are working on new merger guidelines that could replace the current Horizontal Merger guideline and provide more guidance on vertical merger enforcement (the FTC rescinded the existing vertical guidelines last year). Given all this, we expect the trends of hostility and uncertainty to magnify in the near future.Hear from other experts and dive into the numbers in the 2021 Second Request Trends Report.antitrust; ediscovery-reviewediscovery-review, digital-forensics, antitrusthsr-second-requests; blog; mergersbill mariano