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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

As the amount of data generated by companies exponentially increases each year, leveraging artificial intelligence (AI), analytics, and machine learning is becoming less of an option and more of a necessity for those in the eDiscovery industry. However, some organizations and law firms are still reluctant to utilize more advanced AI technology. There are different reasons for the reluctance to embrace AI, including fear of the learning curve, uncertainty around cost, and unknown return on investment. But where this is uncertainty, there is often great opportunity. Adopting AI provides an excellent opportunity for ambitious legal professionals to act as the catalysts for revitalizing their organization’s or law firm’s outdated eDiscovery model. Below, I’ve outlined a simple, five-step process that can help you build a business case for bringing on cutting-edge AI solutions to reduce cost, lower risk, and improve win rates for both organizations and law firms.Step 1: Find the Right Test CaseYou will want to choose the best possible test case that highlights all the advantages that newer, cutting-edge AI solutions can provide to your eDiscovery program.One of the benefits of newer solutions is that they can be utilized in a much wider variety of cases than older tools. However, when developing a business case to convince reluctant stakeholders – bigger is better. If possible, select a case with a large volume of data. This will enable you to show how effectively your preferred AI solution can cull large volumes of data quickly compared to your current tools and workflows.Also try to select a case with multiple review issues, like privilege, confidentiality, and protected health information(PHI)/personally identifiable information (PII) concerns. Newer tools hitting the market today have a much higher degree of efficiency and accuracy because they are able to run multiple algorithms and search within metadata. This means they are much better at quickly and correctly identifying types of information that would need be withheld or redacted than older AI models that only use a single algorithm to search text alone.Finally, if possible, choose a case that has some connection to, or overlap with, older cases in your (or your client’s) legal portfolio. For a law firm, this means selecting a case where you have access to older, previously reviewed data from the same client (preferably in the same realm of litigation). For a corporation, this just means choosing a case, if possible, that shares a common legal nexus, or overlapping data/custodians with past matters. This way, you can leverage the ability that new technology has to re-use and analyze past attorney work product on previously collected data.Step 2: Aggregate the Data Once you’ve selected the best test case, as well as any previous matters from which you want to analyze data, the AI solution vendor will collect the respective data and aggregate it into a big data environment. A quality vendor should be able to aggregate all data, prior coding, and other key information, including text and metadata into a single database, even if the previously reviewed data was hosted by different providers in different databases and reviewed by different counsel.Step 3: Analyze the Data Once all data is aggregated, it’s time for the fun to begin. Cutting-edge AI and machine learning will analyze all prior attorney decisions from previous data, along with metadata and text features found within all the data. Using this data analysis, it can then identify key trends and provide a holistic view of the data you are analyzing. This type of powerful technology is completely new to the eDiscovery field and something that will certainly catch the eye of your organization or your clients.Step 4: Showcase the Analytical ResultsOnce the data has been analyzed, it’s time to showcase the results to key decision makers, whether that is your clients, partners, or in-house eDiscovery stakeholders. Create a presentation that drills down to the most compelling results, and clearly illustrates how the tool will create efficiency, lower costs, and mitigate risk, such as:Large numbers of identical documents that had been previously collected, reviewed, and coded non-responsive multiple times across multiple mattersLarge percentages of identical documents picked up by your privilege screen (and thus, thrust into costly privilege re-review) that have actually never been coded privilege in any matterLarge numbers of identical documents that were previously tagged as containing privilege or PII information in past matters (thus eliminating the need for review for those issues in the current test case).Large percentages of documents that have been re-collected and re-reviewed across many mattersStep 5: Present the Cost ReductionYour closing argument should always focus on the bottom line: how much money will this tool be able to save your firm, client, or company? This should be as easy as taking the compelling analytical results above and calculating their monetary value:What is the monetary difference between conducting a privilege review in your test case using your traditional privilege screen vs. re-using privilege coding and redactions from previous matters?What is the monetary difference between conducting an extensive search for PII or PHI in your test case, vs. re-using the PII/PHI coding and redactions from previous matters?How much money would you save by cutting out a large percent of manual review in the test case due to culling non-responsive documents identified by the tool?How much money would you save by eliminating a large percentage of privilege “false positives” that the tool identified by analyzing previous attorney work product?How much money will you (or your client) save in the future if able to continue to re-use attorney work product, case after case?In the end, if you’ve selected the right AI solution, there will be no question that bringing on best-of-breed AI technology will result in a better, more streamlined, and more cost-effective eDiscovery program.ai-and-analyticsanalytics, ai-big-data, data-re-use, phi, pii, blog, ai-and-analytics,analytics; ai-big-data; data-re-use; phi; pii; bloglighthouse

This year’s International Women’s Day theme revolves around “breaking the bias” and imagining a more gender-equal world. This topic seems particularly relevant for the legal and technology fields, which both have long histories of being male-dominated industries. In 1980, just 8% of attorneys were women, with that number growing to 37% percent by 2021. While the number of women in the technology field has actually declined over the last 40 years, from 37% in 1985, to 33% in 2022.But cold statistics, while helpful, don’t tell the full story. Numbers can be helpful to get a 10,000-foot view of how far we’ve come and how far we still need to go—but they can’t tell us how to get to that gender-equal world or what it’s like to live those statistics. For that, we need to listen to women in the legal and technology space.We need to understand the perseverance of the women who broke through the glass ceiling when they were one of a few in the profession. Like when Supreme Court Justice Ruth Bader Ginsburg explained how they had to install a women’s bathroom in the justices’ robing room after her appointment to the Supreme Court in 1993. We need to hear the stories of the women who broke barriers while dealing with the intersectionality of gender and racial bias. Like Loretta Lynch, the first African-American woman and second woman to be confirmed as United States Attorney General in 2015, recounting the story of a client who directed all of his questions to Lynch’s co-worker – a young male associate – who had nothing to do with what Lynch was presenting.And we need to listen to the women leading our industry today and paving the way for the next generation. In that vein, Lighthouse is honored to feature seven women who are innovators, champions of equity, and models of leadership in the legal technology field:Vanessa Quaciari, eDiscovery Counsel, Baker Botts L.L.P.Kim Foster, Discovery Services Manager, Lane PowellKelly Clay, Assistant General Counsel and Global eDiscovery Counsel, GSKJani Grantz, eDiscovery Manager, DaVitaMarilyn Caldwell, eDiscovery Director, SiemensMoira Errick, Litigation Support Manager, StripeMargaret Dolson, Global Head eDiscovery Services and Archiving Technology, Deutsche Bank USAWe had the honor of interviewing these women about their experiences in the legal technology field and asking them their thoughts on breaking down biases within the industry. Their perspectives and advice can serve as a helpful guide for all people who strive for equality.Recognize the achievements and contributions of women Recognizing the achievements of women is a simple but powerful tool in the fight to break down bias. When women’s achievements, contributions, and ideas are recognized within a firm or organization, it helps dismantle harmful stereotypes that women are not as present in the workplace, or that they don’t achieve as much as men.Talking other women up is so important. When you have a seat at the table and an opportunity to promote another talented woman – you should always do so. —Margaret DolsonFrom a cultural perspective, you have to be intentional and lead by example. Elevate female voices by echoing their comments and ideas while ensuring they receive full credit for their contributions. Seek out their counsel in front of others, and do it often, so that it becomes the norm within your culture. —Kim FosterHowever, for a variety of reasons, women may not feel comfortable recognizing their own achievements. They may also be more reticent to accept recognition or downplay their contributions. Many of the women we spoke to mentioned that accepting recognition was just as important as giving it, because recognition of one woman serves to amplify the voices of others.Women are far too often dismissive of their own achievements. We don't want to be seen as someone who brags or calls attention to ourselves. Frequently, we fall into the societal trappings of even going so far as to be dismissive of our own accomplishments – if we even make them publicly known. I strive to normalize being proud of ourselves, to share what we have achieved, and know that even if it may seem small to our own eyes, it's an accomplishment. I encourage a safe and supportive environment where everyone can feel free to share in their own way, through their own voice, or through the help of another. We all deserve recognition for what we do. —Moira ErrickI remind women that your achievements may seem like just doing your job, but they are so much more for each of us, and it is important to accept and recognize the appreciation. —Kelly ClayI’ve joined organizations to get my name, knowledge, and experience out there to show what women are capable of and be encouraging to women and other genders. —Jani GrantzTo help facilitate and encourage this recognition, it’s important for firms, organizations, departments, and teams to have a dedicated method for acknowledging achievements, wins, and contributions for all employees. This can be as simple as an email chain, or as formal as a dedicated system.My company as a whole strives for equality in all areas, be it gender, race, or any other identifying factor, and that allows my team the ability to recognize accomplishments from everyone including women. In my department, we do Friday emails where people get shout-outs for their contributions and wins, all inclusive of genders, as everyone’s achievements are important to the growth of the village. —Jani GrantzWe are proud to have extremely talented women throughout our firm and are constantly making sure we help raise their visibility. —Vanessa QuaciariWe celebrate achievements both formally and informally, including day-to-day support and recognition in broader team meetings, postings, and events. —Marilyn CaldwellWork to increase representation Both the legal and technology fields have been historically male-dominated. While the statistics are improving incrementally, there is still a way to go before there is equity in the legal technology industry.Many times in my career, I have been the only woman in the room, in the meeting, in the planning session. —Marilyn CaldwellGenerally speaking, both the legal and technology fields have up to now been male-dominated. Even in the eDiscovery niche, the technological knowhow is typically something that is provided by men. This likely is the result of the relatively low number of women historically graduating with science, technology, engineering, and mathematics (STEM)-related degrees. —Vanessa QuaciariHistorically, there has been a perception that women are not as technically inclined or analytical as men. This is simply not true, evidenced by the many exceptional women in eDiscovery at all levels. The legal and technology fields both suffer from stereotypes of having fewer women in them than many other fields. While more women have been entering law school and the legal field generally, there are fewer women at the higher levels of ownership (partners) and leadership. Women want equitable opportunities for growth and development, and they want to be considered for leadership roles. —Kim FosterOver the years I’ve seen men get bigger matters, better pay, and faster promotions because “historically men know more about technology” and they support their own first. —Jani GrantzThus, the importance of women representation in the industry cannot be understated. A more diverse team is stronger and more innovative. Representation also breaks down barriers and moves organizations toward gender equality.When there are more of us in the room, more women who have a seat at the table and have the ability to influence decision-making, it puts us in a better position to recognize the potential of other women and help move them forward. —Margaret DolsonMore women in leadership positions bring a more well-rounded, balanced, and holistic perspective to business. —Marilyn CaldwellThere are a variety of ways to increase representation of women, both on a small scale and across the entire industry. On a micro level, team members can ensure that there is diversity across projects, matters, and teams. Co-workers can prioritize diversity of thought when setting important meetings. Outside of work, people can strive to improve representation by getting involved in technology and legal education programs or join industry groups dedicated to diversity, equity, and inclusion in the field. On a macro level, organizations should develop systems to ensure their hiring, pay, and career development practices are driving diversity. Companies and firms can also support organizations that are dedicated to increasing diversity in technology and legal education.We get to increased representation in the industry by listening, by intentional discourse, and, most importantly, by supporting and identifying women with talent to fill these roles.—Marilyn CaldwellBreaking gender biases starts at home. I have two daughters and a son, and I try to instill in them all an interest in science and technology, rather than perpetuate the misguided notion that those fields are only appropriate for boys. —Vanessa QuaciariTake stock of your current compensation program (i.e., how are people paid, do we have consistent methodologies to establish pay ranges for a specific role, provide pay increases, etc.). Develop hiring and recruiting protocols that evaluate individuals based on observable skills, measurable outcomes, etc. In hiring, this may entail ensuring that recruiters use similar questions for each candidate, improve validity and reliability within the candidate selection process, and give weight to candidate attributes that actually count and ensure that scorers are consistent. —Kim FosterI personally have worked to change that gender stereotype by increasing my eDiscovery tech knowledge, learning the front and back end of relevant software, getting my RCA, and staying current with legal tech updates. —Jani GrantzBefore implementing these systems at the organizational level, however, decision-makers may need to be trained to understand their own implicit biases to ensure they are not unintentionally hampering diversity efforts. Educate your decision-makers about bias and implicit bias. Decision-makers could include, but are not limited to, your organization’s recruiting team, hiring managers, supervisors, those in leadership roles who hire individuals, including positions responsible for ongoing professional development. —Kim FosterOne of the things I’ve championed within our organization is unconscious bias training and exposure – because I think the awareness of that is what can really lead to change. Discussing unconscious bias and its effects is not about assigning blame. It’s about talking through the things that may cause us to be inherently biased against others, and even ourselves, within the workplace. And that discussion can lead us to shift those perceptions so that everyone feels comfortable expressing their thoughts and opinions. —Margaret DolsonBoldly be yourself… and then don’t be afraid to use your voice loudlyMany high-achieving women often speak about facing “imposter syndrome” – the feeling of doubting your own ability in a role while feeling like a fraud masquerading as a leader. This experience may be exacerbated for women in a male-dominated industry because other leaders and experts in the industry are predominantly men, and therefore, don’t look or sound like they do.One way to overcome this feeling is to recognize the implicit bias you may have around what an “expert” or “leader” looks or sounds like – and then working to stop trying to fit into that mold. In other words, strive to be your authentic self.Imposter syndrome is a very real issue because we may never fit into the template of what a “leader” has traditionally looked and sounded like within the legal and technology industries. So, we end up trying to fit into a mold of someone who is not remotely like us. But when we are able to be our authentic selves, and we know our subject matter – we can show up as competent, charismatic, and confident even when we don’t fit into a blueprint. However, it can take a lot of courage to do that. —Margaret DolsonOnce you are not afraid to use your own voice, you can then start using it loudly – not only to demonstrate your own expertise and knowledge, but also as a voice for others.Present yourself as you are, focusing on your skills and abilities rather than your appearance. Do not be afraid to put yourself “out there” for technical positions or projects, and never let anyone tell you that you are not capable. —Kim FosterContinue to stand up for gender equality and don’t back down whether you’re a woman who is being treated unfairly or someone who is witnessing acts of inequality toward women and other genders. Don’t be afraid to voice your opinion and bring notice to the bias. Even if it’s unintentional, it’s important that people see the affects bias has so that behaviors can be changed. —Jani GrantzDon’t let inertia get you. Speak up, advocate for yourself the way you would for others. Take up more space than you need and keep moving forward. —Kelly ClayThere are very brilliant women who are leading the charge both on the legal and the technological side as well as the judicial side. Day in and day out they are demonstrating through case law, articles, and innovative technology expansion that the traits we prize in the workforce are equal opportunity characteristics that any human can demonstrate passionately. —Moira ErrickLean in. Gather perspective. Be clear. Be diplomatic AND assertive. Be an example. Take a seat at the table. Be brave. Be candid. Listen to understand. —Marilyn CaldwellFind your tribeIt’s important to find your “tribe” – a group of people who support each other and can provide knowledgeable advice and an ear to listen when needed. When women have a support system and feel accepted as they are, they feel comfortable using their voice to advocate for themselves and for others. In this way, women can empower each other to break through barriers and bias.I strongly urge all women to find their tribe. Find a mentor, be a mentor. Be active in both your professional and personal communities in whatever way you can. We don't have to network through these organized functions to be supported. We can support one another on the sidelines of the soccer field, at 3 a.m. on a group text as we cram in one more rewrite of that summary, or at 8 a.m. as we take a moment to ourselves. Find your tribe who will give you the support and respect we all deserve. —Moira ErrickWithin the workplace, I recommend women align themselves with similarly-minded professionals, not only women in leadership positions, but people whose careers and knowledge are worth emulating and understanding. I think this helps break gender biases while creating goodwill with people with similar career paths. —Vanessa QuaciariRecognize the historic challenges women are facing today – and work to overcome thoseCovid-19 has had a dramatic effect on the workforce. But it has had a disproportionate effect on women. For instance, a 2021 policy brief from the International Labour Organization found that globally, women’s employment dropped by 4.2% between 2019 and 2020, compared with 3% for men. And a January 2021 report from the National Women’s Law Center showed that when the economy lost 140,000 net jobs in December of 2020, all of those losses fell on women (with women losing 156,000 jobs and men gaining 16,000). This disproportionate effect is because women are often the primary caregivers in family structures.Covid has impacted all of us profoundly. For caregivers in a family its impact is amplified. I don’t want to assume that all caregivers are women, but many are the primary caregivers and also have full time jobs. —Kelly ClayAs a mother, I am aware of how the pandemic has impacted not only women lawyers with children, but parents in general, who now have their usual load of professional responsibilities plus the added duties related to having their children at home all of the time. —Vanessa QuaciariI have seen many working women, especially those who also act as caregivers, facing a lot of added stress due to biased thinking. I have seen many women who have had to make life altering choices...family or career. Near and dear friends have had to step away from their roles because they are not afforded the trust by their employers to get their jobs done outside of the “correct” hours of the day. Covid has exacerbated that, but by the same token it has brought this issue to the forefront. It's not a problem that is unique to any one company, it is endemic in our nation. —Moira ErrickIndeed, while these hardships were felt most acutely during pandemic-related lockdowns, the pandemic simply highlighted and exacerbated inequities that already existed for women. Moving forward, this can be addressed by looking more holistically at the root cause and working to remedy from the ground up. In terms of how to curb the disproportionate impact of the pandemic as we move forward – we need to shift our focus to include men in this analysis. Rather than solely asking women what they need, we also need to ask men, “What do you need in order to be equal participants in running a household?” Because running a household is very similar to running a business and when we focus only on women, we are saying that it’s solely on a woman to keep that business running. The disproportionate burden on women can’t just be addressed by trying to accommodate women, we need to also bring men into the equation. —Margaret DolsonCorporations that support work life balance, in whatever terms the employee sets, are still unicorns. We have to recognize, as a nation, that the mindset that work can only be done in one location during set hours is simply not true in today's business world and given the disparate impact such restrictions have on women, it should not be tolerated anymore. We cannot close the door to half of the workforce because they are left with no other options due to inability to access childcare, lack of school, partners who also are beholden to unforgiving work schedules, and the many other hurdles that are out there. We need to recognize that work is work, whether it is done between 9 a.m. and 5 p.m. or 7 p.m. to 2 a.m. or any combination thereof, so long as it meets the overarching needs of the business. —Moira ErrickConclusionThe stories and advice of these women leaders can serve as a guide, helping to lead us to become a more gender-equal industry and world. Lighthouse is proud to amplify their voices.diversity-equity-and-inclusionblog, diversity-equity-and-inclusion,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

Legal operations departments aim to support the delivery of legal services in an efficient manner. To that end, resource management and solving problems through technology are core responsibilities of the department. But, the tasks of a legal department vary from answering legal phone calls, filing patents, reviewing and approving contracts, and litigating, just to name a few. With such a varied workload, what to automate can be difficult to identify. To help, I have put together a brief overview of where to start.Step 1: IdentificationStart by identifying the tasks that are repetitive. One of the best ways I have found to do this is to set up a quick 15-minute discussion with 3-5 representatives from different functional areas of your legal team, and from different levels (e.g. individual contributor, manager, function head). In that meeting, ask them one or all of the following questions:What tasks do you wish your team no longer had to do?What tasks do you want to be replaced by robots in the future?What tasks are low value but your team still spends a lot of time on?You should not spend too much time here – the goal is to identify a pretty quick list that is top of mind for people. From these interviews, create a list for further vetting. Just in case you come up empty handed or aren’t able to get time with people within legal, here is a list of items that are commonly automated and we would expect to come up:Contract Automation Self service retrieval of boilerplate contracts (e.g., NDAs) Self service building common contracts (e.g., clause selection for vendor contracts, developer agreements)Request for review, negotiation, and signature of other contractsLegal Team Approvals Marketing document approvals Budget approval for any legal team spend Legal Assistance Requests (Intake) Legal research request Legal advice on an issue neededNeed for outside counselPatent Management Alerts for filing and renewal deadlines Automatically manage workflow for submissionsSelect one or two items from your list and then validate it with your boss and/or general counsel. You want to understand whether others agree on the impact automation will make and identify any potential concerns.Step 2: Build vs. BuyWhether to purchase third-party software or build your own internally is always a good question to start with. Building your own tool gives you exactly what you want with, oftentimes, very little need to change your process. But, it is more resource-intensive both for the build and the maintenance. Buying off the shelf software limits you in what’s commercially available but it takes all the load off your development resources.For some, build or buy may be an easy question as they may not have access to development resources. For others, they may not have any budget for an external tool and/or may be required to use internal teams. For most, however, they fall in the middle and have some access to resources and some budget (but usually not enough of either – that’s a whole other topic).If you fall into this latter category, you will have to analyze your options. Your organizational culture will dictate what depth of analysis is needed. Regardless of the level of detail, the process is the same. The easiest place to start is by surveying what is commercially available. Even if you decide to build, knowing what software is out there, what features are available, and the general costs is helpful. Next, it is helpful to get an approximate cost of the build and maintenance if done internally. This can be a rough order of magnitude based on estimates from other internal tools developed or can be a more detailed estimate developed with the engineering team. Once you have the costs, you will want to add some information about the pros and cons of each solution – e.g., time to build and implement, technology dependencies (if known), other considerations (e.g., we are moving to the cloud in 6 months and we don’t know impact). Once you have this analysis, you can put forth a recommendation to your boss and whomever else is required to decide on how to proceed.Step 3: DesignNow that you have a decision, you can move on to design. This is the most critical stage as this is where you are determining exactly what results your automation will produce. The first thing to do here is to map out your current internal process including who does what. You want to make sure you have a representative of each group take a look at the process diagram and validate it.Once you have the process in place, you’re ready to work with the development team. If you are buying a solution for automation, you should be working closely with the software provider’s onboarding team to overlay your current process with the capabilities of the software. You will want to note where the software does not support your process and where changes will need to be made. If you adjust your process, be sure to involve the same representatives that helped with the initial diagram to provide feedback on any proposed changes in the process.If you are building the solution, you will meet with your internal product resource. This person (or people) will want to understand the process diagram and may even want to watch people go through the process so they can understand user behavior. They will then likely convert your diagram into user stories that developers will develop against. Make sure to be as specific as possible in this process. This resource will be the one representing your voice with the developers so you want them to really understand the nuances of the process.Expect some iteration back and forth during this stage and although I have simplified it here, this will be a long stage and the most important.Step 4: ImplementationThe final stage of the process is implementation. Start with a pilot of the automation. Either select a small use case or a small group of users and validate that your automation functions as planned. During this pilot project, it is really helpful to have resources from your software providers or from the development team readily available to make changes and help answer questions. During this pilot, you should also keep track of how the automation is performing versus your expectations. For example, if you expected it to save time, create a way to track the time it saves and report on that metric.After a successful pilot and necessary refinement, you can move on to your full rollout. Create a plan that includes the deployment of the technology, training, feedback, and adjustment. Make sure to also identify the longer-term maintenance strategy that includes continuing to gather feedback and ways to improve the automation over time.There are lots of great publications that go into further detail about each of the steps above, but hopefully this points you in the right direction. Once deployed, automation can be a very powerful tool that augments your team without adding additional FTEs.To discuss this topic more, please feel free to reach out to me at DJones@lighthouseglobal.com.legal-operations; ai-and-analyticslegal-ops, blog, legal-operations, ai-and-analyticslegal-ops; bloglighthouse

When it comes to data privacy in healthcare fraud investigations and litigation, there is more than HIPAA to consider. Fraud investigations and litigation in the healthcare industry are growing. Whether these matters are handled internally or involve external parties to produce to, increased regulatory scrutiny — coupled with vast amounts of data generated by healthcare organizations — has created a pressing need for such organizations to become more adept at comprehensively inventorying, accessing, and reviewing internal data sources for potential fraud.A perennially tricky issue, and one that is just getting thornier, concerns how to treat sensitive, private data in a healthcare context. Healthcare organizations need to be mindful not only of carefully managing protected health information (PHI) subject to the Health Insurance Portability and Accountability Act (HIPAA), but also protected consumer information, which is now subject to regulations such as the California Consumer Privacy Act. Challenges and costs related to being compliant with these regulations are growing and setting themselves up to be just as substantial as managing privilege in litigation.Healthcare data: What privacy rules apply?To make sure these new compliance requirements do not inadvertently extend timelines or burn through budgets, those managing healthcare fraud matters need to proactively take stock of which regulatory regimes concerning personal data are applicable in their case and what data sets being reviewed in their matter could potentially have personal data subject to regulation.Now there is certainly a gray area in distinguishing between protected health information and protected consumer information in a healthcare context. Technically, information is PHI (and therefore subject to HIPAA) if it is created or received by a healthcare provider or health plan. But in today’s data-driven environment, there are a variety of touchpoints between consumers and healthcare services (e.g., marketing data analytics, customer service records, fitness app logs, fringe benefit tracking) that defy traditional understandings of what exactly differentiates PHI from a broader pool of potentially protected consumer data.So, whether subject to HIPAA or CCPA or other privacy mandates, healthcare companies nowadays need to be able to track potentially protected information across all of their data sources, including those not traditionally considered sensitive in that they do not contain information such as health histories, lab test results, or medical bill information.Healthcare fraud: Muddying the data privacy watersThe nature of healthcare fraud further complicates an approach to identifying and appropriately treating sensitive personal data. Matters related to false claims, physician self-referral, Medicaid/Medicare fraud, improper kick-backs, or non-compliant contract and billing practices (to name a few), most often require delving into internal email communications to understand to what extent a fraudulent pattern exists within the organization under investigation, thus enlarging the data pool subject to privacy mandates.The internal work of sorting out billing and coding issues is a messy affair that involves relaying a variety of details of specific patient treatment across multiple related emails. Methodically tracking how these questions get resolved internally over time is at the heart of good healthcare fraud investigation and litigation practice. And carefully treating the sensitive data involved in these conversations is a responsibility that comes with it. If, for instance, you are relying on techniques to extract personal data that have only been tested on structured electronic medical records, you will be missing data that is potentially protected in relevant email discussions.Similar to the task of finding potentially privileged information in large document sets, identifying and treating personal data in healthcare fraud requires its own dedicated workflow, leveraging a mix of tools and methods. The key to successful identification and treatment of protected personal data is being deliberate about the process you design and implement, and specific about the tools you are integrating into it.data-privacyblog, data-privacybloglighthouse

On July 9, 2021, President Biden signed a sweeping new Executive Order (“the Order”) with the stated goal of increasing competition in American markets. Like the recently issued Executive Order on Improving the Nation’s Cybersecurity, the Executive Order on Promoting Competition in the American Economy is meant to establish “a whole-of-government” approach to tackle an issue that is typically handled by numerous federal agencies. As such, the Order includes 72 initiatives touching more than a dozen federal agencies and numerous industries, including healthcare, transportation, agriculture, internet service providers, technology, beer and wine manufacturing, and banking and consumer finance.Notably, the Order calls on the Department of Justice (DOJ) and Federal Trade Commission (FTC) to “vigorously” enforce antitrust laws and “reaffirms” the government’s authority to challenge past transactions that may have been in violation of antitrust laws and regulations (even if they were not challenged by previous Administrations). The remainder of this blog will broadly outline the contents of the Order and conclude with a brief summary on possible ramifications for organizations undergoing merger and acquisition activity (as well as the law firms that counsel them) and how to prepare for them.What is in the Executive Order on Promoting Competition in the American EconomySection 1: PolicyThis section broadly outlines the benefits of “robust competition” to America’s economy and asserts the U.S policy of promoting “competition and innovation” as an answer to the rise of foreign monopolies and cartels. This section also announces the Administration’s policy of supporting “aggressive legislative reforms” to lower prescription drug prices and supports the enactment of a public health insurance option.Sec. 2: The Statutory Basis of a Whole-of-Government Competition Policy This section outlines the antitrust laws which form the Administration’s whole-of-government anti-competition policy, including the Sherman Act, the Clayton Act, and the Federal Trade Commission Act, as well as fair competition and anti-monopolization laws, including Packers and Stockyards Act, Federal Alcohol Administration Act, the Bank Merger Act, and others.Sect 3: Agency Cooperation in Oversight, Investigation, and RemediesThis section outlines the Administration’s policy of cooperation between agencies on anti-competition issues, stating that when there is overlapping jurisdiction over anticompetitive conduct and mergers, the involved agencies should “endeavor to cooperate fully in the exercise of their oversight authority” to benefit from the respective expertise of the agencies and to improve Government efficiency.Section 4: The White House Competition Council This section establishes a White House Competition Council to “coordinate, promote, and advance” government efforts to address monopolies and unfair competition. The section also mandates that the Council should work across agencies to provide a coordinated response to monopolization and unfair competition and outlines the Council make up and meeting cadence.Section 5: Further Agency Responsibilities This section mandates that the heads of all agencies must “consider using their authorities” to further the anti-competition policies outlined within the Order, and “encourages” relevant positions and heads of agencies (including the Attorney General, Chair of the Federal Trade Commission (FTC), Secretary of Commerce, and others) to enforce existing antitrust laws “vigorously,” as well as review and consider revisions to other laws and powers, including encouragement to:Enforce the Clayton Act and other antitrust laws “fairly and vigorously.Review merger guidelines to consider whether they should be revised.Revise positions on the intersection of intellectual property and antitrust laws.Review current practices and adopt a plan for the revitalization of merger oversight under the Bank Merger Act and the Bank Holding Company Act of 1956.Consider whether to revise the Antitrust Guidance for Human Resource Professionals of October 2016.Consider curtailing the unfair use of non-compete clauses that may unfairly limit worker mobility.Consider rulemaking in other areas such as: Unfair data collection and surveillance practices that may damage competition, consumer autonomy, and consumer privacy; Unfair anticompetitive restrictions on third-party repair or self-repair of items (aimed at restrictions that prevent farmers from repairing their own equipment);Unfair anticompetitive conduct or agreements in the prescription drug industries;Unfair competition in major Internet marketplaces;Unfair occupational licensing restrictions;Unfair exclusionary practices in the brokerage or listing of real estate; andAny other unfair industry-specific practices that substantially inhibit competition.The section also calls upon the Secretary of Agriculture to address the unfair treatment of farmers and improve competition in the markets for farm products, and for the Secretary of the Treasury to assess the conditions of competition around the American markets for beer, wine, and spirits (including improving the market for smaller, independent operations).Notably, this section also calls for the Chair of the Federal Communications Commission to consider adopting “Net Neutrality” rules and other avenues to promote competition and lower prices across the telecommunications ecosystem.Finally, the section also calls for the Secretary of Transportation to protect consumers and improve competition in the aviation industry, including enhancing consumer access to airline flight information, providing consumers with more flight options at better prices, promoting rulemaking around requiring airlines to refund baggage fees, and address the failure of airlines to provide timely refunds for flight cancellations resulting from the COVID-10 pandemic.ConclusionAs a whole, the result of this Order will be that organizations undergoing mergers and acquisition activity can expect to face more scrutiny from the government – and that law firms that provide counsel for those types of transactions can expect that government investigations of those activities (like HSR Second Requests) will be more in-depth and meticulous. Accordingly, any law firms and organizations preparing for those types of investigations would do well to evaluate their eDiscovery technology now, in order to ensure that they are using the best and most up-to-date legal technology and workflows to help locate the data requested by the government more accurately and efficiently.antitrustprism, blog, antitrustprism; blog; antitrustsarah moran

Generative AI. Bard. Bing AI. Large language models. Artificial intelligence continues to dominate headlines and workplace chats across every industry since OpenAI’s public release of ChatGPT in November of 2022. Nowhere was this more evident than at this year’s Legalweek event. The annual conference, which gathers thousands of attorneys, legal practitioners, and eDiscovery providers together in New York City, was dominated by discussions of ChatGPT and AI. This makes sense, of course. Attorneys must understand how major technology shifts will impact their clients or companies—especially those in eDiscovery and information governance who deal with corporate data and its challenges. But there was a slight twist to the discussions about ChatGPT. In addition to the possible impacts and risks to clients who use the technology, there was just as much, if not more, focus on how it could be used to streamline eDiscovery.The idea of using a tool released to the public less than four months ago seems almost ironic in an industry with a reputation for slowly adopting technology. Indeed, a 2022 ABA survey showed that as few as 19.2% of lawyers use predictive coding technology (i.e., technology assisted review or TAR) for document review, up from just 12% in 2018. Even surveys dominated by eDiscovery software providers showed TAR was being used on less than 30% of matters in 2022. Given that the technology behind traditional TAR tools has existed since the 1970s and the use of TAR has been widely accepted (and even encouraged) by court systems around the world for over a decade, these statistics are strikingly low.So, what is driving this recent enthusiasm in the industry around AI? The accessibility and generative results of ChatGPT is certainly a factor. After all, even a child can quickly learn how to use ChatGPT to generate new content from a simple query. But the recent excitement in eDiscovery also seems to be driven by the significant challenges attorneys are encountering:Macroeconomic volatility and unpredictability have been a near constant stressor for both corporate legal departments and law firms. Legal budgets are shrinking, and layoffs have plagued almost every industry, leaving legal teams to do the same volume of work with fewer resources.Corporate legal teams are being pressured to evolve from a cost center to one that generates revenue and savings, while attorneys at law firms are expected to add value and expertise to all sectors of a company’s business, beyond the litigation and legal sectors they’ve traditionally operated in. And all attorneys are facing increasing demand to become experts in the risks and challenges of the ever-evolving list of new technology used by their clients and companies.New technology is generating unprecedented volumes of corporate data in new formats, while eDiscovery teams are still grappling with better ways to collect, review, and produce older data formats (modern attachments, collaboration data, text messages, etc.) In short, even the most technology-shy attorneys may be finding themselves at a technology “tipping point,” realizing that it is impossible to overcome some of these challenges without leveraging AI and other forms of advanced technology. But while the challenges may seem grim, there is an inherent hopefulness in this moment. The legal industry’s tendency to adopt AI technology more slowly than other sectors means there’s ample opportunity for growth. Some forward-thinking legal teams, with the help of eDiscovery providers, have already been leveraging advanced AI technology to substantially increase the efficiency and accuracy of eDiscovery workflows. This includes tools that utilize the technology behind ChatGPT, including large language models and natural language processing (NLP). And unlike ChatGPT, where privacy concerns have already been flagged regarding its use, existing AI solutions for eDiscovery were developed specifically to meet the stricter requirements of the legal industry—with some already overcoming tough scrutiny from regulators, opposing counsel, and courts. In other words, the big eDiscovery question of 2023 may not be, “Can ChatGPT revolutionize eDiscovery in the future?” but rather, “What can advanced AI and analytic tools do for eDiscovery practitioners right now?” While the former is up for debate, there are definitive answers to the latter threaded throughout many of the other major industry discussions happening now. Some of those discussions include:If you want to go far, go together Today’s larger and more complicated data volumes often make the traditional eDiscovery model feel like the proverbial round hole that the square (data) peg was not designed to fit into. And it’s becoming increasingly expensive for legal teams to try to do so. To operate in this new era, it’s essential to work with partners who can help you meet your data needs and align with your goals. A good example is when an in-house legal team partners with a technology-forward law firm and eDiscovery provider to build a more streamlined and modern eDiscovery program. This kind of partnership provides the resources, expertise, and technology needed to take a more holistic approach to eDiscovery—breaking away from the traditional model of starting each new matter from scratch. These teams can work together to create and deploy tools and expertise that reduce costs and improve review outcomes across all matters. For example, customized AI classifiers built with data and work product from the company’s past matters, cross-matter analytics that identify review and data trends, and tailored review workflows to increase efficiency and accuracy for specific use cases. This partnership approach is a microcosm of how different organizations and teams can work together to overcome common industry challenges. Technology that meets us where we areDespite all the chatter around ChatGPT, there is currently no “easy AI button” to automate the document review process. However, modern eDiscovery technology (including advanced AI) can be integrated into almost every stage of the document review process in different ways, depending on a case team’s goals. This technology-integrated approach to eDiscovery workflows can help case teams achieve unprecedented efficiency and review accuracy, mitigate risks of inadvertently producing sensitive documents, minimize review redundancy across matters, and quickly pull out key themes, timelines, and documents hidden within large data volumes. Technology-forward law firms and managed review partners can help case teams integrate advanced technology and specialized expertise to achieve these goals in a defensible way that works with each company’s existing data and workflows. The only constant is change The days of a static, rarely updated information governance program are gone. The nature of cloud data, the speed of technology evolution and adoption, and the increasingly complex patchwork of data privacy and security regulations mean that legal and compliance teams need to be nimble and ready for the next new data challenge. New generative AI tools like ChatGPT may only add to this complexity. While this type of technology may be largely off limits in the near future for eDiscovery providers and law firms due to client confidentiality, data privacy, and AI transparency issues, companies in other industries have already begun using it. Legal and compliance teams will need to ensure that any new data created by generative AI tools follow applicable data retention guidelines and regulations and begin to think through how this new data will impact eDiscovery workflows.The furor and excitement over the potential use cases for ChatGPT in eDiscovery are a hopeful sign that more legal practitioners are realizing the potential of AI and advanced analytic technology. This change will help push the industry forward, as more in-house teams, outside counsel, and eDiscovery providers partner together to overcome some of the industry’s toughest data challenges with advanced technology.For other stories on practical applications of AI and analytics in eDiscovery, check out more Lighthouse content. lighting-the-way-for-review; ai-and-analytics; lighting-the-path-to-better-reviewai-big-data, blog, ai-and-analytics,ai-big-data; blogsarah moran

Cloud computing dates to the mid-1990s – so why is this relatively old concept still such a hot topic? Haven’t we figured it all out by now? And isn’t the benefit of today’s SaaS cloud environments that someone else, namely the SaaS provider, handles software management? What else is there to figure out? Having spent the last several months talking to legal, compliance, and IT professionals about their Microsoft 365 environments, I am confident that there is still a lot that corporate IT departments are grappling with. In fact, a recent survey conducted by Lighthouse of 106 IT managers and executives found that although most organizations had a change management process in place for on-premises feature updates and upgrades, and most organizations planned to have change management in place for enterprise-wide SaaS technology updates in the next five years, only 16% had something in place today.[1] To better harness this technology as it continues to evolve and to minimize risks along the way, it’s important to understand why these change management gaps exist, what their impact is, and how legal and IT teams can work together in new ways to close them.Managing the Evolution of SaaSThe adoption of enterprise SaaS cloud technologies has only become prevalent in the last decade and growth has skyrocketed over the last couple of years. In fact, Microsoft 365 had 23.1 million consumer subscribers five years ago (Fiscal Year 2016) and that number has grown to 58.4 million. As such, IT organizations have not had to support SaaS enterprise offerings at scale until very recently and today most IT departments are supporting both on-premises and SaaS cloud environments. The first priority in supporting this explosive adoption was to implement and migrate over to the new system. It is only recently that focus has shifted toward governance and processes around these systems.Even with a newer focus on process, one of the touted benefits of SaaS cloud technology is less maintenance and software support by the in-house IT team. Of course, there is the need to set up process to resolve user questions and to ensure systems have been set up to facilitate the business running properly. But, planning and executing hardware or software upgrades is mostly managed by a third-party provider so there is not an urgent need to set up robust change management. In addition, the old change management process where major developments are analyzed, tested, and timed for deployment to desktops still applies to Microsoft 365.However, using the old process for new applications can have drawbacks. First, not all updates that Microsoft or others make are configurable updates where there is a choice on how, and whether, to implement. Second, if users are logging into a web environment (as opposed to desktop apps), IT teams don’t necessarily have control over the version their users are utilizing. Finally, given that most organizations have differing levels of IT permissions, meaning some groups are upgraded sooner than others, teams must move quickly to handle unpredictable and varied update schedules. With the speed and variability of new feature updates, the old process may not be agile enough to handle them. The differences between SaaS and on-premises environments (where you have full control of the upgrade schedule) can leave some gaps even when organizations review, analyze, and test the roadmap and updates from the Microsoft Message center.The old process often fails to prepare the business for these changes because IT, legal, and other teams are not always communicating about the broader risk or implementation implications. Because the IT team is focused on availability and scalability, it often misses how certain changes can introduce business risks outside of their ken. Solely relying on IT professionals to determine the broader impact of updates can mean that business, regulatory, and other risks outside of IT’s awareness are overlooked.Measuring the Impact of UpdatesWhether these management gaps are tolerable is a risk decision that each organization must make—one that can put the user experience in tension with a developed IT process. In discussions with legal, compliance, and information governance professionals that focus on SaaS services, handling the cadence and speed of these updates is a concern that keeps them up at night. But, quickly providing users new features has considerable benefits for the business too. It’s important for IT to prioritize ensuring that users can access their business data and that the business can continue without interruption over cumbersome update management.When weighing these risks and benefits it’s important to fully appreciate their potential impacts. An example of where these priorities conflict is highlighted in a change around Microsoft Teams meeting transcripts. In March 2021, Microsoft made an update that allows for a live transcript of certain Teams meetings. In November 2021, Microsoft expanded that functionality to Teams Channel meetings and upgraded the features of live transcripts to include name attribution to the speaker. This is helpful functionality for users and, given that it is an automatic upgrade, there may be little to do from an IT perspective. From a risk and legal perspective, however, there are a couple of key considerations. First, where is the transcript stored after the meeting and do retention policies apply? Second, is the data subject to ongoing regulatory or litigation requests and how is it accessed? The answers to those questions are complicated by the fact that the location of the data depends on whether a user downloaded the transcript after the meeting. Many IT organizations caught this change by reviewing the Microsoft Message center for updates—and in doing their own testing they determined that disabling the functionality was the best course of action. This was an update with obvious data ramifications that outweighed the potential benefits in a risk assessment from both IT and legal. For updates that are less obvious, IT may not have consulted legal. For updates where the value to users may seem to outweigh the risk, where the risks aren’t initially apparent, or when there are no configuration options—IT may have a more challenging decision to make.Reimagining a Change Management ProcessHaving a cross-functional framework in place to discuss and implement these types of updates is key to managing changes. Many organizations have some sort of accountability in place around updates—an individual or group of people are responsible for reviewing the Microsoft Message center. Although this structure is lower in cost and requires fewer resources, it has a few drawbacks. First, if only IT is involved, you may have only one perspective on the impacts of updates and that can be too narrow to determine the effects on the broader business. Second, many organizations do not have a tracking mechanism to determine what Microsoft updates they have read, evaluated, tested, and taken action against. With dozens of messages, many of which don’t need action, it is easy to lose track of what has been evaluated. Finally, if there isn’t clear accountability with dedicated resources the process can lose legitimacy and fail. Organizations who choose to minimize their business risk do not have to put in place a heavy structure to manage updates. In fact, the process around on-premises software upgrades can easily be adapted to the cloud situation.The single most important thing that an IT team can do for an effective SaaS support practice is to adapt and enforce existing change management and organizational controls. More specifically, IT organizations should consider:Dedicating a resource to track and review changes from service and cloud providers to ensure updates and changes are properly evaluated for risk and business continuity.Relying on a robust change management system with stakeholders throughout the organization to provide clearly articulated approval, risk identification, testing, and risk management.Partnering with your compliance team to ensure adherence to governance frameworks, organizational commitments, and client requirements. The compliance function is trained to manage risk and is uniquely chartered with authority and independence with a company’s governing body.Collaborating with legal. Lawyers are trained to spot issues and manage risk for the entire business. Often times, individual departmental stakeholders are responding to team-level incentives. Legal teams are also learning to adapt their governance structures to evolving cloud solutions.Leveraging the Project Management Office to ensure that stakeholders and risks are identified at the start of any specific project (i.e., measure twice, cut once).One of the most effective ways to get the right stakeholders’ input is to create a Change Approval Board (“CAB”) with subject matter experts from every business group to meet on a periodic basis. The CAB provides a framework that ensures IT has input from across the business while still giving it the opportunity to own and manage the support of the software.One of the benefits of SaaS technologies is the ability to utilize and optimize with the newest features and to take some of the hardware management burden off IT. By putting in place a cross-functional team to review and manage the update process, you can mitigate your organizational risk while allowing users take full advantage of the benefits.[1] In February 2022, Lighthouse surveyed 106 IT managers or above who had Microsoft on-premises and now have Microsoft 365. The survey found that only 16% had implemented a change management process for M365 and 62% of organizations planned to implement one in the next 5 years.microsoft-365; chat-and-collaboration-data; information-governancemicrosoft, cloud-migration, cloud-services, blog, microsoft-365, chat-and-collaboration-data, information-governance,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

Below is a copy of a featured article written by Jennifer Swanton of Medtronic, Shannon Capone Kirk of Ropes & Gray, and John Del Piero of Lighthouse for Legaltech News.This is the second article in a two-part series, designed to help create a better relationship between corporate attorneys and advanced technology. In our first article, we worked to demystify the language technology providers tend to use around AI and analytics technology.With the terminology now defined, we will now focus on six specific ways that corporate legal teams can put this type of technology to work in the eDiscovery and compliance space to improve cost, outcome, efficiencies.1. Document Review and Data Prioritization: The earliest example of how to maximize the value of analytics in eDiscovery was the introduction of TAR (technology-assisted review). CAL (or continuous active learning) allows counsel to see the most likely to be relevant documents much earlier on in the process than if they had been simply looking at search term results, which are not categorized or prioritized and are often overbroad. Plainly put, it is the difference between an organized review and a disorganized review.Data prioritization offers strategic value to the case team, enabling them to get to the crux of a case earlier in the process and ultimately develop a better strategic plan for cost and outcomes. This process also offers the ability to get to a point of review where the likelihood of additional relevant information is so low, no new review is needed. This will save time and money on large document review projects. Such prioritization is critical for time-sensitive internal investigations, as well.To dive further into the Pandora analogy we used above: if you were to listen to a random shuffle of songs on Pandora without giving feedback on what you like and don’t like, you’d likely listen for days to encounter several songs you love. Whereas, if you give Pandora feedback, it learns and you’re likely to hear several songs you love within hours. So why suffer days of listening to show tunes and harp solos when what you really love is the brilliant artistry found in songs by the likes of Ray LaMontagne?2. Custodian and Data Source Identification: Advanced analytics that can analyze complex concepts within data can be a powerful tool to clearly identify your relevant data custodians, where that data lives, and other data sources worth considering. Most conceptual analytics technology can now provide real-time visibility into information about custodians, including the date range of the data collected and the data types delivered. More advanced technology that also analyzes metadata can provide you with a deeper understanding of how custodians interact with other people, including the ability to analyze patterns in timing and speech, and even the sentiment and tone of those interactions.All of this information can be used to help quickly determine whether or not a prospective custodian has information relevant to the case that needs to be collected, or if any supplemental collections are required to close a gap in the date range collected. This, in turn, will help reduce the amount of collections required and minimize processing time in fast-paced cases. These tools also help determine which data sources are likely to hold your most relevant information and where supplemental collections may be warranted.Above: Brainspace display of communication networks, which enable users to identify custodians of interest, as well as related people and conversations.3. Identifying Privileged and Personal Information: Another powerful way to leverage analytics in the eDiscovery workflow is to identify privileged documents in a far more cost-effective way than we could in the past. New privilege categorization software creates significant efficiencies by analyzing the text, metadata, and previous coding of documents in order to categorize documents according to the likelihood that they are actually privileged.More advanced analytics tools can now identify documents that have been flagged as privileged by traditional privilege term screens, but have a high likelihood of not containing privileged communications. For example, the technology identifies that the document was sent to a third-party (thus breaking the privilege attorney-client privilege) or because the only privilege term within the document is contained within a boilerplate footer.These more advanced analytics tools can be much more effective at identifying privileged documents than a privilege search term list, and can help case teams successfully meet rolling production deadlines by pushing the documents that are less likely to be privileged (i.e. those that require less privilege review) to the front of the review line. When integrated with other eDiscovery applications, you can also create a defensible privilege log that can be produced for the litigation team.Additionally, flagging potential PII and protected intellectual property (IP) caught up in a large data set can be challenging, but analytics technology provides in-house legal teams with an important ally for automating those processes. Advanced analytics can streamline the process of locating and isolating this sensitive data, which is often hiding in a variety of different systems, folders, and other information silos. Tools allow you to flag Health Insurance Portability and Accountability Act (HIPAA) protected information based on common format and structure to help quickly move through documents and accurately identify and redact needed information.4. Information Governance: One of the high-stakes elements of large data collections is the importance of parsing out highly sensitive records, such as those that contain PII and protected IP. This information is incredibly important to protect company data and also to comply with the growing number of data privacy regulations worldwide, including Europe’s General Data Protection Regulation (GDPR), the California Consumer Protection Act (CCPA), and HIPAA. Analytics can help identify and flag documents per their appropriate document classification. This can be helpful for both the business in their day-to-day operations as well as the legal team in responding to requests.5. Data Re-Use: One of the largest potentials with the use of analytics is the ability to save time and money on your next matter. Technologically advanced companies are now starting to use analytics technology to integrate previous attorney work product, case information, and documents across all organization matters. On a micro level, recycling and analyzing previous work product allows companies to stop re-inventing the wheel on each case and aids in much faster identification of privilege, personal information, and non-responsive documents.For example, organizations often pay to store documents that contain previous privilege tagging from past matters in inactive or archived databases. Those documents, sitting unused in storage, can be separately re-ingested and used to train a privilege model in the new matter, allowing legal teams to immediately eliminate documents that were identified as privileged in previous reviews—even prior to any human coding in the new matter.On a macro level, this type of advanced capability enables organizations to make data-driven decisions across their entire eDiscovery landscape. Rather than looking at each new matter on an individual basis in a singular lens, legal teams can use advanced analytics to analyze previously coded data across the organization’s entire legal portfolio. This can provide previously unheard of insights, like which custodians often contain the most privileged documents matter over matter, or if a data source rarely produces responsive documents. Data re-use can also come in handy in portfolio matters that have overlapping custodians and data sets and need common production. The overall results are more strategic legal and data decisions, more favorable case outcomes, and increased cost efficiency.6. Accuracy: Finally, and potentially the most important reason to use analytics tools, is to increase accuracy and have a better work product. Studies have shown that tools like predictive coding are more accurate than human work product. That, coupled with the potential for cost savings, should be all one needs to utilize these technologies.As useful as these new analytics tools are to in-house legal teams in their efforts to manage eDiscovery today, it is important to understand that the great promise of these technologies is the fact that they are in a state of continuous improvement. Because analytics tools learn, they refine and “get smarter” as they review more data sets. We all know that we’re on just the cusp of what analytics will bring to our profession—but we believe the future of this technology in the area of eDiscovery management is here now.ai-and-analyticstar-predictive-coding, blog, corporate, ai, ai-and-analytics,tar-predictive-coding; blog; corporate; aijohn del piero

As the corporate workplace continues to evolve—encompassing hybrid work environments, bring your own device policies, and cloud-based storage—companies are well-advised to consider areas of increased vulnerability and whether their policies, procedures, and forensic tools are keeping pace with reality. A hybrid or remote workforce and a more collaborative data infrastructure only exacerbate data risks that were easier to manage when employees were comfortably situated at their desks. Adding even more complexity to these risks are broader labor trends, including “the Great Resignation and Reshuffle” and an aging work force, which are changing staffing and recruiting strategies and impacting knowledge transfer and IP creation.Employee intake and departure: crucial points of data security Two areas likely needing renewed attention are the moments of employee onboarding and offboarding, when a company’s most prized assets—people and data—are on the move. Departing employees present a particular risk as the potential for data exfiltration of IP and other sensitive information, whether intentional or not, is high. Often, employees take corporate IP with them inadvertently, a situation bound to get worse as turnover rates grow (Gartner anticipates a 20% jump in turnover from the pre-pandemic national average).Since people usually take jobs similar to the ones they leave (and often with competitors), taking company data along with their coffee mug and potted plant may seem justified (I wrote this stuff, so it’s mine)—or simply inconsequential. Cloud storage services such as Dropbox, Box, or Google Drive, and collaborative apps such as Microsoft Teams or Slack make it all the easier to appropriate files, lending credence to a feeling of personal data ownership. No matter how it happens, the escape into the wild of proprietary items such as source code, strategy documents, contact lists, and financial information exposes the company to untold risk, including the danger of running afoul of any number of privacy regulations if personal data is exfiltrated from its protected environment—an additional headache for the company if things go south. Are current entry and exit protocols enough? Although most companies have entrance and exit protocols usually siloed as HR and IT functions, the recent surge in employee turnover has put those very teams under pressure as they face their own personnel and budget deficits. Further, responsibilities have become less defined at a time when offboarding tasks—many now carried out at a distance—should be fortified to include proactive data monitoring and oversight, activities such teams may not be equipped to handle. The challenge, of course, is the growing complexity of the data landscape. Knowing what information is where, who accesses it, and for what purpose becomes more difficult to track as software and storage options grow, yet this is key to keeping important data protected. Data security: start training early and reinforce often Onboarding procedures can play a key role in keeping data where it belongs and helping employees navigate through and understand their responsibilities in this increasingly intricate data terrain. First, a sound onboarding protocol can ensure that new employees aren’t bringing troublesome data into the environment. No company wants to deal with the fallout of being in possession of some other company’s IP or sensitive information. More importantly, onboarding offers the most opportune time to clearly communicate expectations regarding data management and safety—information that should be reinforced with frequent (and up to date) training that emphasizes data protection and ownership. It's easy to forget as time goes on what data may be confidential or sensitive, and even easier to forget that data belongs to the business, not the employee. In short, data awareness should be instilled as part of the company culture right from the start. Seize the moment: identify and monitor offboarding risksThe recent and ongoing workplace disruption calls for a hard look at offboarding data risks and an evaluation of potential vulnerabilities to protect data before an employee leaves the company, bolster the exit protocols to have in place when they do, and have the proper forensic and analytic tools to handle data monitoring and address potential wrongdoing. Most companies do have standard offboarding checklists that address employee assets, data access, and preservation obligations as they leave the company. But there’s more to data protection at this crucial moment than ticking off boxes. Expand and optimize the offboarding checklistSavvy companies implement a more proactive, programmatic approach that begins earlier, with monitoring procedures that include defensible and repeatable processes to guard against the exfiltration of company data while helping to fortify the company’s position in case of a breach. A few important things to consider as part of the offboarding process:Know which employees warrant departure attention. Develop risk profiles with business stakeholders to identify which classes of employees, whether based on role, circumstance of departure, seniority, or access to sensitive information could present an exfiltration risk.Understand the company’s data landscape. Make sure there are mechanisms in place for tracking where sensitive data and IP may reside and when such data has been accessed.Explore activity and assets with the employee prior to their departure. An expert, friendly review of a departing employee’s recent computer activity with the employee, including an audit of their recent network activities, use of peripherals, cloud uploads, and email sends, can reveal and help mitigate potential trouble.Preserve employee devices and data as warranted with state-of-the-art forensic tools. Forensic preservation is critical to ensuring valid evidence down the line, especially since investigations today regularly involve new and novel devices, data sources, and artifacts that must be diagnosed and understood.Document all offboarding information. A paper trail of findings during the exit procedure is important if further analysis is recommended or necessary and will be crucial for subsequent investigation, if it comes to that. Have a plan if there is evidence of wrongdoing. Part of any data security effort is having an action plan to execute if there are signs of a breach. Preservation, collection, and a forensic analysis may be required should legal action ensue. ConclusionThe recent upheaval in employee turnover along with more collaboration tools and storage options present increasing risk for today’s enterprise. Companies that acknowledge new vulnerabilities and leverage opportunities to revamp outdated policies and protocols are better positioned to stop data exfiltration before it becomes a problem. The best solution: Implement robust onboarding and offboarding solutions that include data monitoring, reporting, and forensic analysis to enable a quick pivot to actionable remediation steps if trouble is brewing. digital-forensics; information-governancedeparting-onboarding-employee, blog, risk-management, digital-forensics, information-governancedeparting-onboarding-employee; blog; risk-managementdaniel black

Increasingly, opportunities for cloud-based collaboration and efficiencies, and challenges presented by the rapid proliferation of complex data, are incentivizing organizations to transform their corporate data governance and eDiscovery operations from traditional self-managed infrastructure to the Microsoft 365 (M365) Cloud. Benefits in terms of convenience, security, robust functionality, and native capabilities related to eDiscovery and compliance are the primary drivers of this move.While there are many benefits to moving into the M365 ecosystem, it requires legal and compliance teams to take on new considerations regarding the constant evolution that characterizes cloud software. With continually changing applications, establishing static workflows for eDiscovery, legal holds, data dispositions, and other legal operations is not enough. As the M365 software and functionality changes, workflows must be constantly evaluated to ensure their validity, relevance, and defensibility.Exacerbating this challenge is the reality that the traditional IT change management paradigm designed to preemptively address cross-organizational considerations (including impacts to legal, compliance, and eDiscovery operations) does not fit the Cloud/SaaS framework. Organizations must now rethink their change management approach as they modernize with M365.This is the first in a series of blog posts devoted to highlighting key changes that have been released into the M365 production environments. One of the biggest challenges for organizations is identifying which of the myriad of updates pose potential risks to eDiscovery operations. Distinguishing the changes that do and do not pose a significant eDiscovery impact can be extremely difficult unless the reviewer has some level of subject-matter expertise and understands the specific workflows deployed within the organization. Here are some common scenarios with potential eDiscovery impact that could easily go unnoticed by the untrained eye:Updates that create a new data sourceUpdates that change a backend data storage locationUpdates altering the risk profile of features that were previously disabled due to legal / privacy riskUpdates that render an existing eDiscovery process obsoleteEach subsequent blog post in this series will highlight an example of a software update related to our key software scenarios, detailing the nature of the change, the potential impact, as well as when and why organizations should care.microsoft-365; chat-and-collaboration-data; information-governancemicrosoft, compliance-and-investigations, blog, cloudcompass, advisory-services, microsoft-365, chat-and-collaboration-data, information-governance,microsoft; compliance-and-investigations; blog; cloudcompass; advisory-serviceslighthouse

Below is a copy of a featured article written by Jennifer Swanton of Medtronic, Shannon Capone Kirk of Ropes & Gray, and John Del Piero of Lighthouse for Legaltech News.Despite the traditional narrative that lawyers are hesitant to embrace technology, many in-house legal departments and their outside service providers are embracing the use of what is generally referred to as artificial intelligence (AI). In terms of litigation and internal investigations, this translates more specifically into conceptual analytics and predictive coding (also referred to as continuous active learning, or CAL), which are two of the more advanced technological innovations in the litigation space and corporate America.This adoption, in part, seems to be driven by an expectation from corporate leaders that their in-house counsel must be able to identify and utilize the best available technology in order to drive cost efficiency, while also reducing risk and increasing effective and defensible litigation positions. For instance, in a 2019 survey of 163 legal professionals conducted by ALM Intelligence and LexisNexis, 92% of attorneys surveyed planned to increase their use of legal analytics in the upcoming 12 months. The reasoning behind that expected increase in adoption was two-fold, with lawyers indicating that it was driven both by competitive pressure to win cases (57%), as well as client expectation (56%).Given that the above survey took place right before the COVID-19 pandemic hit, it stands to reason that the 92% of attorneys that expected to increase their use of analytics tools in 2020 may actually be even higher now. With a divisive election and receding pandemic only recently behind us, and an already unpredictable market, many corporations are tightening budgets and looking to further reduce unnecessary spend. Conceptual analytics and CAL are easy (yes, really) and effective ways to manage ballooning datasets and significantly reduce discovery, litigation and internal investigation costs.With that in mind, we would like to help create a better relationship between corporate attorneys and advanced technology with the following two step approach—which we will outline in a series of two articles.This first installment will help demystify the language technology providers tend to use around AI and analytics technology so that in-house teams feel more comfortable with adoption. In our second article, we will provide examples of some great use cases where corporate legal teams can easily leverage technology to help improve workflows. Together, we hope this approach can help in-house legal teams adopt technology that drives efficiency, lowers cost, and improves the quality of their work.Demystifying AI JargonIf you have ever discussed AI or analytics technology with a technology provider, you are probably more than aware that tech folks have a tendency to forget that the majority of their clients don’t live in the world of developing and evaluating new technology, day in and day out. Thus, they may use terms that are often confusing to their legal counterparts (and sometimes use terms that don’t match what the technology is capable of in the legal world). For this reason, it is helpful to level set with some common terminology and definitions, so that in-house attorneys are prepared to have better, more practical real-world discussions with technology providers.Analytics Technology: Within the eDiscovery and compliance space, analytics technology is the ability of a machine to recognize patterns, structures, concepts, terminology, and/or the people interacting within data, and then present that analysis in a visual representation so that attorneys have a better overview of their data. As with AI, not all analytics tools have the same capabilities. Vendors may label everything from email threading identification to more advanced technology that can identify complex concepts and human sentiment as “analytics” tools.Within these articles, when we reference this term, we are referring to the more advanced technology that can analyze not only the text within data but also the metadata and any previous coding applied by subject matter experts. This is an important distinction because this type of technology can greatly improve the accuracy of the analysis compared to older tools. For example, analytics technology that can analyze metadata as well as text is much better at identifying concepts like attorney-client privilege because it can analyze not only the language being used but who is using that language and the circumstances in which they use it.Artificial Intelligence (AI): Probably the most broadly recognized term due to its prevalence outside of the eDiscovery space, AI is technically defined as the ability of a computer to complete tasks that usually would require human intelligence. Within the eDiscovery and compliance world, vendors often use the term broadly to refer to a variety of technologies that can perform tasks that previously would require completely human review.It is important to remember though that the term AI can refer to a broad range of technology with very different capabilities. “AI” in the legal world is currently being used as a generalized term and legal consumers of such technologies should press for specifics—not all “AI” is the same, or, in several cases, even AI at all.Machine Learning: Machine learning is a category of algorithms used in AI that can analyze statistics and find patterns in large volumes of data. The algorithms improve with experience—meaning that as documents are coded in a consistent fashion by humans, the better and more accurate the algorithms should become at identifying specific data types. Note here that there is a common misunderstanding that machine learning requires large amounts of data from which to learn. That is not necessarily true—all that is required for machine learning to work well is that the input it learns from (i.e., document coding for eDiscovery purposes) is consistent and accurate.Natural Language Processing (NLP): NLP is a subset of AI that uses machine learning to process and analyze the natural language humans use within large amounts of data. The result is technology that can “understand” the contents of documents, including the context in which language is used within them. Within eDiscovery, NLP is used within more advanced forms of analytics technology to help identify specific content or sentiments within large datasets.For example, NLP can be used to more accurately identify sensitive information, like personally identifiable information (PII), within datasets. NLP is better at this task than older AI technology because older models relied on “regular expressions” (a sequence of characters to define a search pattern) to identify information. When a “regular expression” (or regex) is used by an algorithm to find, for example, VISA account numbers—it will be able to identify the correct number pattern (i.e., any number that starts with the number 4 and has 16 digits) within a dataset but will be unable to differentiate other numbers that have the same pattern (for example, employee identification numbers). Thus, the results returned by legacy technology using regex may be overbroad and include false positives.NLP can return more accurate results for that same task because it is able to identify not only the number pattern, but can also analyze the language used around the pattern. In this way, NLP will understand the context in which VISA account numbers are communicated within that dataset compared to how employee identification numbers are communicated, and only return the VISA numbers.Predictive Coding (also referred to as Technology-Assisted Review or TAR): Predictive coding is not the same as conceptual analytics. Also, predictive coding is a bit of a misnomer, as the tools don’t predict or code anything. A human reviewer is very much involved. Simply put, it refers to a form of machine learning, wherein humans review documents and make binary coding calls: what is responsive and what is non-responsive. This is similar in concept to selecting thumbs up or down in Pandora so as to teach the app what songs you like and don’t like. After some human coding and calibrations between the human and the tool, the technology uses the human’s coding selections to score how the remaining documents should be coded, enabling the human to review the high scored documents first.In the most current versions of predictive coding, this technology continually improves and refreshes as the human reviews, which reduces or eliminates the need for surgical precision on coding at the start (which was a concern in the former version of predictive coding and why providers and parties spent a considerable amount of time concerned with “seed sets”). This improved and self-improving prioritization of large document sets based on high-scored documents is usually a more efficient and organized manner in which to review documents.Because of this evolution in predictive coding, it is often referred to in a host of different ways, such as TAR 1.0 (which requires “seed sets” to learn from at the start) and TAR 2.0 (which is able to continually refresh as the human codes—and is thus also referred to as Continuous Active Learning or CAL). Some providers continue to use the old terminology, or explain their advancements by walking through the differences between TAR 1.0 and TAR 2.0, and so on. But, speaking plainly, in this day and age, providers and legal teams should really only be concerned with the latest version of TAR, which utilizes CAL, and significantly reduces or totally eliminates the previous concern with surgical precision on coding an initial “seed set.” With our examples in the next installment, we hope to illustrate this point. In a word, walking through the technological evolution around predictive coding and all of the associated terminology can cause unnecessary intimidation, and can cause confusion between providers, parties and the court.The key takeaway from these definitions is that even though all the technology described above may technically fall into the “AI” bucket, there is an important distinction between predictive coding/TAR technology and advanced analytics technology that uses AI and NLP. The distinction is that predictive coding/TAR is a much more technologically-limited method of ranking documents based on binary human decisions, while advanced analytics technology is capable of analyzing the context of human language used within documents to accurately identify a wide variety of concepts and sentiment within a dataset. Both tools still require a good amount of interaction with human reviewers and both are not mutually exclusive. In fact, on many investigations in particular, it is often very efficient to employ both conceptual analytics and TAR, simultaneously, in a review.Please stay tuned for our next installment in this series, “Analytics and Predictive Coding Technology for Corporate Attorneys: Six Use Cases”, where we will outline six specific ways that corporate legal teams can put this type of technology to work in the eDiscovery and compliance space to improve cost, outcome, efficiencies.ai-and-analyticstar-predictive-coding, blog, corporate, ai, ai-and-analytics,tar-predictive-coding; blog; corporate; ailegaltech news

Big data sets are the “new normal” of discovery and bring with them six sinister large data set challenges, as recently detailed in my colleague Nick’s article. These challenges range from classics like overly broad privileged screens, to newer risks in ensuring sensitive information (such as personally identifiable information (PII) or proprietary information such as source code) does not inadvertently make its way into the hands of opposing parties or government regulators. While these challenges may seem insurmountable due to ever-increasing data volumes (and also tend to keep discovery program managers and counsel up at night) there are new solutions that can help mitigate these risks and optimize workflows.As I previously wrote, eDiscovery is actually a big data challenge. Advances in AI and machine learning, when applied to eDiscovery big data, can help mitigate and reduce these sinister risks by breaking down the silos of individual cases, learning from a wealth of prior case data, and then transferring these learnings to new cases. Having the capability to analyze and understand large data sets at scale combined with state-of-the-art methods provides a number of benefits, five of which I have outlined below.Pinpointing Sensitive Information - Advances in deep learning and natural language processing has now made pinpointing sensitive content achievable. A company’s most confidential content could be laying in plain sight within their electronic data and yet be completely undetected. Imagine a spreadsheet listing customers, dates of birth, and social security numbers attached to an email between sales reps. What if you are a technology company and two developers are emailing each other snippets of your company’s source code? Now that digital medium is the dominant form of communication within workplaces, situations like this are becoming ever-present and it is very challenging for review teams to effectively identify and triage this content. To solve this challenge, advanced analytics can learn from massive amounts of publically available and computer-generated data and then fine tuned to specific data sets using a recent breakthrough innovation in natural language processing (NLP) called “transfer learning.” In addition, at the core of big data is the capability to process text at scale. Combining these two techniques enables precise algorithms to evaluate massive amounts of discovery data, pinpoint sensitive data elements, and elevate them to review teams for a targeted review workflow.Prioritizing the Right Documents - Advanced analytics can learn both key trends and deep insights about your documents and review criteria. A normal search term based approach to identify potentially responsive or privileged content provides a binary output. Documents either hit on a search term or they do not. Document review workflows are predicated on this concept, often leading to suboptimal review workflows that both over-identify documents that are out of scope and miss documents that should be reviewed. Advanced analytics provide a range of outcomes that enable review teams to create targeted workflow streams tailored to the risk at hand. Descriptive analysis on data can generate human interpretable rules that help organize documents, such as “all documents with more than X number of recipients is never privileged” or “99.9% of the time, documents coming from the following domains are never responsive”. Deep learning-based classifiers, again using transfer learning, can generalize language on open source content and then fine-tune models to specific review data sets. Having a combination of analytics, both descriptive and predictive, provides a range of options and gives review teams the ability to prioritize the right content, rather than just the next random document. Review teams can now concentrate on the most important material while deprioritizing the less important content for a later effort.Achieving Work-Product Consistency - Big data and advanced analytics approaches can ensure the same document or similar documents are treated consistently across cases. Corporations regularly collect, process, and review the same data across cases over and over again, even when cases are not related. Keeping document treatment consistent across these matters can obviously be extremely important when dealing with privilege content – but is also important when it comes to responsiveness across related cases, such as a multi-district litigation. With the standard approach, cases are in siloes without any connectivity between them to enable consistent approaches. A big data approach enables connectivity between cases using hub-and-spoke techniques to communicate and transit learnings and work-product between cases. Work product from other cases, such as coding calls, redactions, and even production information can be utilized to inform workflows on the next case. For big data, activities like this are table stakes.Mitigating Risk - What do all of these approaches have in common? At its core, big data and analytics is an engine for mitigating risk. Having the ability to pinpoint sensitive data, prioritize what you look at, and ensure consistency across your cases is a no-brainer. This all may sound like a big change, but in reality, it’s pretty seamless to implement. Instead of simply batching out documents that hit on an outdated privilege screen for privilege review, review managers can instead use a combination of analytics and fine-tuned privilege screen hits. Review then occurs from there largely as it does today, just with the right analytics to inform reviewers with the context needed to make the best decision.Reducing Cost - The other side of the coin is cost savings. Every case has a different cost and risk profile and advanced analytics should provide a range of options to support your decision making process on where to set the lever. Do you really need to review each of these categories in full, or would an alternative scenario based on sampling high-volume and low-risk documents be a more cost-effective and defensible approach? The point is that having a better and more holistic view of your data provides an opportunity to make these data-driven decisions to reduce costs.One key tip to remember - you do not need to try to implement this all at once! Start by identifying a key area where you want to make improvements, determine how you can measure the current performance of the process, then apply some of these methods and measure the results. Innovation is about getting a win in order to perpetuate the next.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-analyticsdigital-forensics, ai-and-analyticsanalytics; ai-big-data; data-re-use; blogkarl sobylak

Recently, I had the pleasure of appearing as a guest on Season 5, Episode 1 of the Law & Candor podcast, hosted by Lighthouse’s Rob Hellewell and Bill Mariano. The three of us discussed cloud migrations and how that process can provide a real opportunity for an organization to transform its approach to information governance. Below is a summary of our conversation, including best practices for organizations that are ready to take on this digital and cultural cloud transformation process.Because it is difficult to wrap your head around the idea of a cloud transformation, it can be helpful to visualize the individual processes involved on a much smaller scale. Imagine you are simply preparing to upgrade to a new computer. Over the years, you have developed bad habits around how you store data on your old computer, in part because the tools on that computer have become outdated. Now that you’re upgrading, you have the opportunity to evaluate your old stored data to identify what is worth moving to your new computer. You also have the opportunity to re-evaluate your data storage practice as a whole and come up with a more efficient plan that utilizes the advanced tools on your new computer. Similarly, the cloud migration process is the best opportunity an organization has to reassess what data should be migrated, how employees interact with that data, and how that data flows through the organization before building a brand new paradigm in the Cloud.You can think of this new paradigm as the organization’s information architecture. Just like a physical architecture where the architect designs a physical space for things, an organization’s information architecture is the infrastructure wherein the organization’s data will reside. To create this architecture effectively, you first must analyze how data flows throughout the company. To visualize this process, imagine the flow of information as a content pipeline: you’ve got a pile of papers and files on your desk that you want to assess, retain what is useful to you, and then pass on to the next person down the pipe. First, you would identify the files you no longer need and discard those. Next, you would identify what files you need for your work and put those aside for yourself. Then you would pass the remaining pile down to the next person in the pipeline, who has a different role in the organization (say, accountant). The accountant will pull out the files that are relevant to their accounting work, and pass the files down to the next person (say, a lawyer). The lawyer performs the same exercise for files that are relevant to their legal role, and so on until all the files have a “home.”In this way, information architecture is about clearly defining roles (accounting role, legal role, etc.) and how those roles interact with data, so that there is a place in the pipeline for the data they utilize. This allows information to flow down the pipeline and end up where it belongs. Note how different this system is from the old information governance model, where organizations would try to classify information by what it was in order to determine where it should be stored. In this new paradigm, we try to classify information by how it is used – because the same piece of content can be used in multiple ways (a vendor contract, for example, can be useful to both legal and accountant roles). The trick to structuring this new architecture is to place data where it is the most useful. Going hand-in-hand with the creation of a new information architecture, cloud migrations can (and should) also be an opportunity for a business culture transformation. Employees may have to re-wire themselves to work within this new digital environment and change the way they interact with data. This cultural transformation can be kicked off by gathering all the key players together and having a conversation about how each currently interacts with data. I often recommend conducting a multi-day workshop where every stakeholder shares what data they use, how they use it, and how they store it. For example, an accountant may explain that when he works on a vendor contract, he pulls the financial information from it and saves it under a different title in a specific location. A lawyer then may explain that when she works on the same vendor contract, she reviews and edits the contract language, and saves it under a different title to a different location. This collaborative conversation is necessary because, without it, no one in the organization would be able to see the full picture of how information moves through the organization. But equally important, what emerges from this kind of workshop is the seeds of culture transformation: a greater awareness from every individual about the role they play in the overall flow of information throughout the company and the importance of their role in the information governance of the organization. Best Practices for Organizations: Involve someone from every relevant role in the organization in the transformation process (i.e. everyone who interacts with data). If you involve frontline workers, the entire organization can embrace the idea that the cloud migration process will be a complete business culture transformation.Once all key players are involved, begin the conversation about how each role interacts with data. This step is key not only for the business cultural transformation, but also for the organization to understand the importance of doing the architecture work.These best practices can help organizations leverage their cloud migration process to achieve an efficient and effective information governance program. To discuss this topic further, please feel free to reach out to me at JHolliday@lighthouseglobal.com. information-governancemicrosoft-365, legal-operationscloud; information-governance; cloud-migration; bloglighthouse

Big data can mean big problems in the ediscovery and compliance world – and those problems can be exponentially more complicated when personal data is involved. Sifting through terabytes of data to ensure that all personal information is identified and protected is becoming an increasingly more painstaking and costly process for attorneys today.Fortunately, advances in artificial intelligence (AI) and analytics technology are changing the landscape and enabling more efficient and accurate detection of personal information within data. Recently, I was fortunate enough to gather a panel of experts together to discuss how AI is enabling legal professionals in the ediscovery, information governance, and compliance arenas to identify personal protected information (PII) and personal health information (PHI) more quickly within large datasets. Below is a summary of our discussion, along with some helpful tips for leveraging AI to detect personal information.Current Methods of Personal Data Identification Similar to the slower adoption of AI and analytics to help with the protection of attorney-client privilege information (compared to the broader adoption of machine learning to identify matter relevant documents), the legal profession has also been slow to leverage technology to help identify and protect personal data. Thus, the identification of personal data remains a very manual and reactive process, where legal professionals review documents one-by-one on each new matter or investigation to find personal information that must be protected from disclosure.This process can be especially burdensome for pharmaceutical and healthcare industries, as there is often much more personal information within the data generated by those organizations, while the risk for failing to protect that information may be higher due to healthcare-specific patient privacy regulations like HIPAA.How Advances in AI Technology Can Improve Personal Data Identification There are a few ways in which AI has advanced over the last few years that make new technology much more effective at identifying personal data:Analyzing More Than Text: AI technology is now capable of analyzing more than just the simple text of a document. It can now also analyze patterns in metadata and other properties of documents, like participants, participant accounts, and domain names. This results in technology that is much more accurate and efficient at identifying data more likely to contain personal information.Leveraging Past Work Product: Newer technology can now also pull in and analyze the coding applied on previous reviews without disrupting workflows in the current matter. This can add incredible efficiency, as documents previously flagged or redacted for personal information can be quickly removed from personal information identification workflows, thus reducing the need for human review. The technology can also help further reduce the amount of attorney review needed at the outset of each matter, as it can use many examples of past work product to train the algorithms (rather than training a model from scratch based on review work in the current matter).Taking Context into Account: Newer technology can now also perform a more complicated analysis of text through algorithms that can better assess the context of a document. For example, advances in Natural Language Processing (NLP) and machine learning can now identify the context in which personal data is often communicated, which helps eliminate previously common false hits like mistakenly flagging phone numbers as social security numbers, etc.Benefits of Leveraging AI and Analytics when Detecting Sensitive DataArguably the biggest benefit to leveraging new AI and analytics technology to detect personal information is cost savings. The manual process of personal information identification is not only slower, but it can also be incredibly expensive. AI can significantly reduce the number of documents legal professionals would need to look through, sometimes by millions of documents. This can translate into millions of dollars in review savings because this work is often performed by legal professionals who are billed at an hourly rate.Not only can AI utilization save money on a specific matter, but it can also be used to analyze an entire legal portfolio so that legal professionals have an accurate sense of where (and how much) personal information resides within an organization’s data. This knowledge can be invaluable when crafting burden arguments for upcoming matters, as well as to better understand the potential costs for new matters (and thus help attorneys make more strategic case decisions).Another key benefit of leveraging AI technology is the accuracy with which this technology can now pinpoint personal data. Not only is human review much less efficient, but it can also lead to mistakes and missed information. This increases the risk for healthcare and pharmaceutical organizations especially, who may face severe penalties for inadvertently producing PHI or PII (particularly if that information ends up in the hands of malevolent actors). Conducting quality control (QC) with the assistance of AI can greatly increase the accuracy of human review and ensure that organizations are not inadvertently producing individuals’ personal information. Best Practices for Utilizing AI and Analytics to Identify Personal DataPrepare in Advance: AI technology should not be an afterthought. Before you are faced with a massive document production on a tight deadline, make sure you understand how AI and analytics tools work and how they can be leveraged for personal data identification. Have technology providers perform proof of concept (POC) analyses with the tools on your data and demonstrate exactly how the tools work. Performing POCs on your data is critical, as every provider’s technology demos well on generic data sets. Once you have settled on the tools you want to use within your organization, ensure your team is trained well and is ready to hit the ground running. This will also help ensure that the technology you choose fits with your internal systems and platforms.Take a Global Team Approach: Prior to leveraging AI and analytics, spend some time working with the right people to define what PII and PHI you have an obligation to identify, redact, or anonymize. Not all personal information will need to be located or redacted on every matter or in every jurisdiction, but defining that scope early will help you leverage the technology for the best use cases.Practice Information Governance: Make sure your organization is maintaining proper control of networks, keeping asset lists up to date, and tracking who the business and technical leads are for each type of asset. Also, make sure that document retention policies are enforced and that your organization is maintaining controls around unstructured data. In short, becoming a captain of your content and running a tight ship will make the entire process of identifying personal information much more efficient.Think Outside the Box: AI and analytics tools are incredibly versatile and can be useful in a myriad of different scenarios that require protecting personal information from disclosure. From data breach remediation to compliance matters, there is no shortage of circumstances that could benefit from the efficiency and accuracy that AI can provide. When analyzing a new AI tool, bring security, IT, and legal groups to the table so they can see the benefits and possibilities for their own teams. Also, investigate your legal spend and have other teams do the same. This will give you a sense of how much money you are currently spending on identifying personal information and what areas can benefit from AI efficiency the most.If you’re interested in learning more about how to leverage AI and analytic technology within your organization or law firm, please see my previous articles on 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.data-privacy; ai-and-analyticsai-and-analytics, microsoft-365analytics; data-privacy; ai-big-data; bloglighthouse

One of the unanticipated consequences of the COVID-19 pandemic and the ensuing shift of office employees being forced to work from home, is the impact on counsel who must continue to direct forensically defensible collections for eDiscovery, investigations, and regulatory response scenarios. As employees adjust to remote work, they are increasingly commingling personal data sources, home networks, and corporate data, which in turn creates a wealth of new data sources that will need to be collected as potentially-relevant ESI.In my recent webinar, I discussed this significant shift to the “new normal” of digital digital-forensics and how information governance policies and IT security practices should be proactively extended to remote employees, as well as ways to mitigate future complications around forensic collections that will now need to be almost exclusively remote. Here are a few of the most important aspects to consider on how working from home impacts digital digital-forensics, and practical workflow strategies for handling remote ESI collections.Working from Home: The Digital digital-forensics ImpactThere’s a behavioral impact that automatically comes with working entirely from home, with less delineation between the workday and home life, and subsequently more temptation to use your work laptop for personal reasons. This behavioral impact is also mirrored in the reverse scenario where personal devices become more convenient to use for work. Although we were already seeing quite a bit of intermingling of data pre-COVID-19, this habit is dramatically increasing as home has quickly become the only workplace and there hasn’t been time for organizations to adopt new IT policies to tackle these issues.With the advent of this new remote workplace era, data (mis)management will remain with us for future matters and there will be a permanent impact on collections going forward. Among the top adjustments that need to be made is custodian questionnaires must be enhanced to scrutinize whether any relevant work-related data or communications reside on the custodians’ home devices. The same scrutiny will need to be applied to personal data potentially residing on work laptops as the opportunities for this type of data intermingling or “contamination” will undoubtedly continue to increase.ESI Collections: Practical Workflow StrategiesEven though we’re currently not able to travel onsite to acquire device and data source evidence, we can continue collections by relying on sound and defensible forensic remote strategies that are already in place. Collections from the Cloud are status quo and conducted remotely by definition, but for other ESI sources, we will favor targeted and logical collections over full physical forensic images.For remote collections on premise at an office that’s closed, if there’s a skeleton IT crew in place, screen sharing can be utilized to mimic the exact scenario of a digital-forensics professional being onsite to help load a hard drive or provide access into a server. For custodians sitting at home, the same process can apply and technical guidance can be provided remotely. If shipping is a safety concern, data can be uploaded by secure encrypted file transfer protocol (FTP) using software that can resume broken uploads or by utilizing fast data transfer solutions such as Aspera. Whether figuring out a safe way to transport encrypted hard drives back and forth or using remote data transfer technology, we’ll need to plan for increased turnaround times due to varying upload speeds from home and/or decontamination procedures that are implemented for shipping protocols.Key TakeawaysAs company and personal custodian data commingling grows during COVID-19, a permanent shift is happening in digital digital-forensics and eDiscovery. From a legal standpoint, it’s settled that company-related communication on personal devices is subject to discovery, thus custodian interviews and other information-gathering techniques to identify the relevant scope of a collections effort must be enhanced. And although data preservation and evidence acquisition tasks may take longer to conduct when onsite collections is not an option, the technology is already in place to ensure forensically sound and defensible remote collections now and in the future.To discuss this topic further, please feel free to reach out to me at JBui@lighthouseglobal.com.digital-forensics; information-governancelegal-operations, digital-forensics, information-governancecloud; collections; cloud-security; 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

The project included replacing expensive third-party archives with native tools in M365, utilizing an automation solution that Lighthouse had recently prototyped for a large global manufacturer, and other breakthroughs the institution was unable to make before engaging with Lighthouse. Our work with the institution helped unblock their Microsoft 365 deployment and ultimately led to disclosure to regulators for institution’s intent to use M365 as system of record.SIFIs have long wished for a better way to meet their mutability requirement. Historically, they have relied on archiving solutions, which were designed years ago and are poorly suited for the data types and volume we have today. For years, people in the industry have been saying, “Someday we’ll be able to move away from our archives.” It wasn’t until the introduction of M365 native tools for legal and compliance that “someday” became possible.Data Management for SIFIs is Exceptionally ComplexThe financial services industry is one of the most highly regulated and litigious sectors in the world. As a result, companies tend to approach transformation gradually, adopting innovations only after technology has settled and the regulatory and legal landscape has evolved.However, the rate of change in the contemporary world has pushed many financial heavyweights into a corner: They can continue struggling with outdated, clunky, inadequate technologies, or they can embrace change and the disruption and opportunities that come with it.From an eDiscovery perspective, there are three unique challenges: (1) as a broker-dealers, they have a need to retain certain documents in accordance with specific regulatory requirements that govern the duration and manner of storage for certain regulated records, including communications (note that the manner of storage must be “immutable”). This has traditionally required the use of third-party archive solutions that has included basic e-discovery functionality. (2) As a highly regulated company with sizable investigation and litigation matters, they have a need to preserve data in connection with large volumes of matters. Traditionally, preservation was satisfied by long-term retention (coupled by immutable storage) and without deletion. Today, however, companies seek to dispose of legacy data—assuming it is expired and not under legal hold—and are eager to adopt processes and tools to help in this endeavor. (3) They have a need to collect and produce large volumes of data—sometimes in a short timeframe and without the ability to cull-in-place. This means they are challenged by native tooling that might not complete the scale and size of their operations. This particular company’s mission was clear: to use M365 as a native archive and source of data for eDiscovery purposes. To meet this mission, Lighthouse needed to establish that the platform could meet immutability and retrievability requirements—at scale and in the timeframe needed for regulatory and litigation matters. Lighthouse Helps a Large Financial Institution Leverage M365 to Replace Its Legacy Archive SolutionLighthouse is perfectly positioned to partner with financial services and insurance organizations ready to embrace change. Many on our team previously held in-house legal and technology roles at these or related organizations, including former in-house counsel, former regulators, and former heads of eDiscovery and Information Governance. Our team’s unique expertise was a major factor in earning the trust and business of a major global bank (“the Bank”). The Bank first engaged with Lighthouse in 2018, when we conducted an M365 workshop demonstrating what was possible within the platform—most notably, at the time, the potential for native tools to replace their third-party archives. Following the workshop, the Bank attempted, together with Microsoft, to find a viable solution. These efforts stalled, however, due to the complexity of the Bank’s myriad requirements. In 2020, the Bank re-engaged Lighthouse to supports its efforts to fully deploy Exchange and Teams and, in doing so, to utilize the native information governance and e-discovery toolset, paving way for the Bank to abandon its use of third-party archiving tools for M365 data. Our account team had the nuanced understanding of industry regulations, litigation and regulatory landscape, and true technical requirements needed to support a defensible deployment.As a result, we were able to drive three critical outcomes that the bank and Microsoft had not been able to on their own: (1) A solution adequate to meeting regulatory requirements (including immutability and retrievability). (2) A solution adequate to meeting the massive scale required at an institution like this. (3) A realistic implementation timeline and set of requirementsLighthouse Ushers the Bank Through Technical and Industry MilestonesWe spent six months designing and testing an M365-based solution to support recording keeping and e-discovery requirements for Teams and Exchange (including those that could support the massive scalability requirements). The results of these initial tests identified several gaps that Microsoft committed to close. The six month marked a huge milestone for the financial services industry, as the Bank disclosed to regulators their intent to use M365 as system of record. This showed extreme confidence in Lighthouse’s roadmap for the Bank, since a disclosure of this nature is an official notice and cannot be walked back easily. Over the next few months, we continued to design and test, partnering with Microsoft to create a sandbox environment where new M365 features were deployed to the Bank prior to general availability, to ensure we were able to validate adequate performance. During this time, Microsoft made a series of significant updates to extend functionality and close performance gaps to meet the Bank’s requirements. Finally, in February 2021, all the Bank’s requirements had been met and they went live with Teams—the first of their M365 workload deployments. That configuration of M365 met only some of the Bank’s need, however, so Lighthouse had to enable additional orchestration and automation on top. As it happens, we had recently done this for another company, creating a proof of concept for a reusable automation framework designed to scale eDiscovery and compliance operations within M365. Building on this work, we were able to quickly launch development of a custom automation solution for the Bank. This project is currently underway and is slated to complete in June, coinciding with their deployment of Exchange Online.Lighthouse Enables Adoption of Teams and Exchange and Scales M365 Compliance FunctionalityCompliant storage of M365 communications using native tools, rather than a third-party archive. Scaled and efficient use of M365 eDiscovery, including automation to handle preservation and collection tasks rather than manual processes or simple PowerShell scripts.Improved update monitoring, replacing an IT- and message-center-driven process with a cross-functional governance framework based on our CloudCompass M365 update monitoring and impact assessment for legal and compliance teams.Framework for compliant onboarding of new M365 communication sources like Yammer. Framework for compliant implementation of M365 in new jurisdictions, including restricted country solutions for Switzerland and Monaco. Framework to begin expanding to related use cases within M365, such as compliance and insider risk management. Lighthouse Paves the Way for Broader M365 Adoption Across the Financial Services IndustryFollowing the success of this project, we have been engaged by a dozen other large financial institutions interested in pursuing a similar roadmap. The roadblocks we removed for the Bank are shared across the sector, so the project was carefully watched. With the Bank’s goals confidently achieved and even surpassed, its peers are ready to begin their own journey to sunset their archives and embrace the opportunities of native legal and compliance tools in M365.

Two Months to Tackle Three Million DocumentsA financial institution with an urgent matter had two months to review 3.6M documents (2.4TB of data).With that deadline, any time that reviewers spent on irrelevant documents or unnecessary tasks risked missing their deadline. So outside counsel called on Lighthouse to help efficiently review documents.AI and Experience Prove Up to the ChallengeUsing our AI-powered review solution, we devised an approach that coordinated key data reduction tactics, modern AI, and search expertise at different stages of review.Junk Removal and Deduplication Set the Stage We started by organizing the dataset with email and chat threading and removing 137K junk documents. Then we shrank the dataset further with our proprietary deduplication tool, which ensures all coding and redactions applied to one document automatically propagate to its duplicates. AI Model Removes 1.5 Million Nonresponsive Documents To build the responsive set, we used our AI algorithm, built with large language models for sophisticated text analysis. We trained the model on a subset of documents then applied it to all 2.2M TAR-eligible documents, including transcripts from chat platforms. The model identified 80% of the documents containing responsive information (recall) with 73% accuracy (precision). The final responsive set consisted of 650K family-inclusive documents—18% of the 3.6M starting corpus. AI Supports Privilege Detection, QC, and Descriptions Our AI Privilege Review solution supported reviewers in multiple ways.First, we used a predictive AI algorithm in conjunction with privilege search terms to identify and prioritize potentially privileged documents for review. During QC, we compared attorney coding decisions with the algorithm’s assessment and forwarded any discrepancies to outside counsel for final privilege calls. For documents coded as privileged, we used a proprietary generative AI model to draft 2.2K unique descriptions and a privilege log legend. After reviewing these, attorneys left nearly 1K descriptions unchanged and performed only light edits on the rest.Search Experts Surface the 300 Documents Most Important for Case Prep Alongside the production requirements for the Second Request, Lighthouse also supported the institution’s case strategy efforts. Each tranche of work was completed in 4 days and within an efficient budget requested by counsel, who was blown away by the team’s speed and accuracy. Using advanced search techniques and knowledge of legal linguistics, our experts delivered: 130 documents containing key facts and issues from the broader dataset, for early case analysis. 170 documents to prepare an executive for an upcoming deposition. Beating the Clock Without Sacrificing Cost or QualityWith Lighthouse Review—including the strategic use of state-of-the-art AI analytics—outside counsel completed production and privilege logging ahead of schedule. The financial institution met a tough deadline while controlling costs and achieving extraordinary accuracy at every stage.

Searching for Evidence in 8TB of Chat and Technical Data Senior executives at an information technology company suspected that former employees had utilized company resources and intellectual property when starting a rival company. To determine whether litigation was called for, executives needed to find the most relevant documents within 8TB of processed data. The data was extremely complex, dating back 6+ years and consisting mostly of Slack data and attachments including highly technical documents, applications, logs, and related system files—tallying over ten million files. The company engaged a senior partner at an AM50 law firm, who recommended using keyword search terms, filters, and targeted linear review to find the “smoking gun” documents—which was estimated to take several months. The company came to Lighthouse looking for a faster, more strategic search alternative for their investigation. Pinpointing Key Docs with Linguistic Analysis Two Lighthouse search and linguistics experts met with company executives to learn exactly what information they suspected the former employees had misappropriated. From there, our experts created linguistic-based search criteria that go well beyond keywords, taking into consideration the unique vocabulary and syntax of software engineers and developers, the conversational quirks of Slack and other chat-based communications, and the coded language used by people who are trying to get away with something. The team delivered documents in 2 batches, refining their search based on input from the executives—and resulting in only 39 files for the company to review. Getting Results—and a Start on Case Strategy—in Days In less than 10 days, 2 Lighthouse experts pierced the subterfuge in the employees’ chat messages to reveal patterns in their behavior and attempts to cover their tracks. In all, we found 39 documents representing possibly questionable conduct, which required only 141 hours of eyes-on review. In comparison, using conventional analytics would have identified 5-20% of the search population as key documents—up to 50K documents to review in this matter. So in the end, Lighthouse saved the company over 3 months and nearly $200K.Armed with knowledge of the key events, timelines, and context of conversations buried within the data, the company was primed to begin litigation efforts and had a team ramped up to perform additional searches when needed.Lighthouse KDI vs Linear Review

Firms Needed Fast Analysis of 25M Documents More than a dozen international law firms—including a Joint Defense Group (JDG) of 11 firms and several firms representing defendants outside the JDG—were engaged in a complex cluster of cases spanning over 30 US jurisdictions. The total document tranche included over 25M documents. The firms needed to find and understand the key players, timelines, and nuances involved in each litigation, while also preparing for hundreds of depositions, witness interviews, hearings, and trials scheduled across the litigation universe. However, traditional approaches to fact-finding and litigation (i.e., document review, keyword searches, etc.) were drowning case teams in extraneous and duplicative information. They came to Lighthouse looking for a strategic, unified approach to fact-finding, led by experts who could deliver the key documents, information, and details the case teams needed—and nothing more. Custom Workflows Power Consistency, Speed, and Efficiency Our experts started by creating a topic map across matters, which helped them quickly provide case teams with the core themes in each jurisdiction while reducing redundant search work. From there, as case strategy for each matter developed, the Lighthouse team drilled down into more nuanced fact-finding to help surface the documents case teams needed to learn the key details of each matter, through strategies like: State/Jurisdictional Overview Workflow – We used advanced search technology to target key documents in incoming productions and categorize them by jurisdiction, providing case teams with an immediate thematic overview of key facts and timelines. Re-Deployable Linguistic Model Workflow – Lighthouse linguists developed models based on intimate knowledge of the language used within the datasets, then deployed them within proprietary search technology to sort documents into tiers based on the likelihood that they contained key information. Deposition Kit Bundle Workflow – By bundling deposition kit requests from the same jurisdictions and departments together, we could search across smaller collections of documents and take a deponent-agnostic approach. Previously Delivered Name Hit Workflow – We provided case teams with documents from previously delivered results, giving them an advanced start on deposition preparation while further reducing duplicative searching. These repeatable workflows significantly reduced the volume of searching and coordination required across matters and enabled Lighthouse experts to quickly zero-in on the exact documents needed—without wasting counsels’ time with redundant and unimportant documents. Critical Docs Found and Delivered Across Dozens of Matters and Hundreds of Kits Over the course of two years, Lighthouse experts prepared dozens of case teams for complex litigation and handled a deluge of competing deadlines, priorities, and ad hoc requests (totaling as many as 70 requests at a time). For the Joint Defense Group, this meant: Over 1,150 deposition kits across 24 matters, encompassing 245K unique documents Over 100 state overviews across 21 different jurisdictions, encompassing 80K documents For law firms representing individual defendants, Lighthouse provided an additional:150 deposition kits, encompassing 13K documents 30 defensive overviews across 20 jurisdictions, encompassing 6K documents 1.3K documents in response to ad hoc requests and trial support Each delivery was limited to essential information—including key themes and players in every jurisdiction, potential gaps in productions, lists of hot/sensitive documents and potential deponents, and key strategy documents—and avoided redundant and unimportant documents. The combination of innovative workflows and cutting-edge technology enabled Lighthouse to keep our team small and consistent throughout the engagement, so the entire effort was achieved by a handful of Lighthouse experts with institutional knowledge of every matter. Since this engagement, we have used the same workflows for other clients facing complex Multidistrict Litigation (MDL)—making Lighthouse key document identification one of the most valuable and scalable litigation technology solutions on the market today.

The Lighthouse team of SMEs applied their dedication to exemplary customer experience and unique strategy of marrying compliance, security, IT, and legal needs to help a global chemistry solutions and specialty material producer meet the ever-evolving security and compliance demands and challenges facing international manufacturing and regulations to effectively deploy Microsoft Purview across workstreams while preparing for needs and reducing costs. Global Leader in Chemistry Solutions Transforms Enterprise Data Protection with Microsoft Purview An international producer of commercial chemicals and specialty materials upholds a commitment to people safety and well-being as part of their core tenets. As cyber risks increased along with data volumes, the organization extended their commitment to safety to include the security of data accessed, produced, and stored within their enterprise. Now, the company has implemented a comprehensive data protection program using the entire Microsoft 365 Information Protection suite. After careful design, the team is piloting the solution before a global rollout. A Commitment to Physical and Digital Safety As one of the world’s largest acetyl products manufacturers and a top-tier producer of high-performance engineered polymers, the company supplies chemicals across major industries and for a variety of industrial and consumer applications. Over 10,000 employees in offices, technical centers, and 50+ manufacturing facilities work to realize a vision of improving the world and everyday life through people, chemistry, and innovation—with products that impact the lives of millions. For the organization, an operational approach rooted in well-being has always meant physically safe working environments for employees, and safe solutions for their customers and their communities. However, in this digital age, they have expanded their notion of safety to include data protection for employees, customers, shareholders, and the communities in which they operate. The company’s Chief Information Security Officer (CISO) notes that committing to data protection means a “higher level of assurance—making sure that our security controls keep pace with the threats that surround us every day and seek to exploit vulnerabilities in companies like us every day. You can’t stand still. You always have to evolve—you always have to get better, otherwise you’re devolving, and you’re getting worse, and becoming more vulnerable.” Advancing Data Protection with a Trusted Partner A few years ago, when the company decided to make the move to the cloud, they chose Microsoft 365 E5 and Microsoft Azure, building on their longstanding use of Microsoft technologies. Prior efforts to overhaul their data protection program had been unsatisfactory. However, with access to new Microsoft Purview capabilities, the Information Security team saw an opportunity to try again. They hoped to utilize the full breadth of the Microsoft 365 Information Protection suite including Information Protection Classification and Labeling, Data Loss Prevention (DLP), and Insider Risk Management solutions. Microsoft tapped Security Solutions and Advanced Specialization Designation-Information Protection and Governance Partner Lighthouse Global to lead the engagement for their ability to effectively understand complex compliance needs across IT, security, and legal departments. They hoped that together they could develop a solution to realize the investment they’d made in Microsoft 365, and to support their corporate commitment to safety for both employees and customers. “If you were to interview a bunch of companies, those who have actual, very successful DLP and data labeling programs typically have a hodgepodge of solutions that get melded together,” reflected the CISO, “and that’s where Lighthouse was successful…we’ve been able to leverage the investment…and get it to work, [and not] have to go spend more money to hodgepodge together a solution.” Developing a Comprehensive, Scalable Solution The Lighthouse team started by holding a series of working sessions to align the company’s vision and requirements and design the implementation approach. Using Microsoft Compliance Check, Lighthouse scanned the company’s environment to get an understanding of current state activity and sensitivity intelligence. The team also reviewed existing policies and approaches for the handling of sensitive data and data loss prevention to identify any areas of opportunity or gaps that could exist. From there, the combined teams were able to successfully design and configure a holistic data protection solution leveraging multiple Microsoft Purview products including Data Loss Prevention, Information Protection, and Insider Risk Management. Starting with data classification, the team defined the sensitive information types that needed to be identified. From there, they developed a set of sensitivity labels corresponding to the data protection policy. This set of classification techniques and labels were generated in the course of both Data Loss Protection and Insider Risk Management implementation, ensuring a comprehensive data life cycle protection program from content identification through insider threat analysis. Finally, the Lighthouse team supported the integration of the Microsoft products with the company’s third-party HR software to feed HR data into the Data Theft by Departing Employee Policy, enabling the creation of a truly end-to-end solution. Fulfilling a Mission of Security The company’s dedication to safety, security, and well-being across applications and contexts drove this project’s success. “Because we see security as part of our commitment to people and innovation, we take a uniquely holistic approach and have strong support all the way up to our board of directors,” says the company’s CISO. The CISO also credits Lighthouse’s unwavering commitment to partnership. “They helped us not only implement the technology and guide us through some of the critical points to consider as we implemented the technology, but also the process and decision points with data—which ultimately, in the end, actually worked,” they conclude. Now, with the design and implementation of the Microsoft Purview-based Data Protection program behind them, the organization’s information security team is focused on operationalizing the program through a series of pilots scheduled over the next year. Their ultimate goal is total, global implementation of the solution—and total, global protection for all employee and customer data. Corporate Case Studymicrosoft; big-datamicrosoft-365; data-privacy

A global biotech achieves consistent and efficient document review with Lighthouse review. Key Actions Coordinating efforts across disparate review teams and counsel Integrating advanced AI and other innovations on an incremental basis ‍ Key Results Streamlined and efficient approach to document review Saving more than $340,000 through a tailored workflow in one recent matter A Lack of Coordination Drove High Costs and Complexity Document review for a global biotech was expensive and inconsistent, due to a high frequency of litigations with often overlapping timelines and different outside counsel. Lighthouse had been managing the company’s electronically stored information (ESI) for years, saving the company hundreds of thousands of dollars through plans and policies introduced over time. After learning of our expertise in managed review, the company hired Lighthouse to bring order and efficiency to that domain as well. Laying a Foundation with Standard Protocols Our first order of business was to establish universal standards across matters, outside firms, and review vendors. These included: Upstream changes , such as data management protocols that made documents easier to search and sort. Overarching review protocols , such as QC process guidelines and specifications for production. Changes to specific tasks , such as refining privilege filters and standardizing coding layouts so review performance could be compared across different matters and teams. A Lighthouse review manager trained all current firms and vendors and was on hand to monitor progress and answer questions, as well as onboard new firms and vendors as needed. Increasing Efficiency Through Technology Over time, Lighthouse gradually introduced accelerators to help increase efficiency and cost savings. Initially, this consisted of: Deduplication improvements , through strategies like single-instance review and normalized deduplication. Review accelerators such as privilege log automation and redaction automation. To drive even more savings, Lighthouse led the company through a test-and-learn process for building workflows around advanced AI and other, more in-depth technology. The process involved trying out a new technology on a live matter, then conducting a post-mortem to clarify what worked and what could be improved. In this way, Lighthouse and the company developed a rubric for determining which workflows were the right fit for different matters. Streamlined, Aligned, and Eager to Keep Innovating In 5 years, Lighthouse transformed the company’s disconnected, manual, expensive approach to document review into a coordinated and robust program that boosts efficiency at every level. For one recent matter—a patent litigation with a tight timeline overlapping the winter holidays—this review program drove extraordinary efficiency and savings. Tailoring the client playbook for the specific matter, the review manager designed a complex workflow that reduced eyes-on review: The initial dataset of almost 8M documents was reduced to a corpus of 388K through deduplication, culling, and removal of embedded and redundant documents. The population was further reduced through search hit only protocols and by employing a continuous active learning (CAL) model, stopping review when responsive documents became scarce. Finally, Lighthouse reunited document family members, automatically giving members tied to responsive documents the coding of their source docs. In the end, Lighthouse: Reduced eyes-on review to just 92K documents (25% of the documents promoted to review) Saved the company an estimated $341,000 in review costs Going forward, the company is ready to increase its use of technology, including classifiers built with advanced AI and an automated workflow for redactions of personally identifiable information (PII). Corporate Case Studyai-and-analytics; ediscovery-reviewediscovery-review, ai-and-analytics, biotech

Lighthouse leveraged linguistic expertise and cutting-edge analytics to efficiently locate only the documents that mattered in a complicated, year-long antitrust criminal investigation and trial. What They Needed Senior executives from a global food manufacturing company faced federal criminal antitrust charges related to allegations of 15 instances of price fixing over a five-year period. A joint defense team comprised of outside counsel representing each of the executives was assembled by the company. The prosecution expected to make rolling productions of evidence up to and through the trial. As those productions rolled in, the joint defense team could tell that many of the evidentiary documents, timelines, and conversations that were key to the prosecution’s case were taken out of context or failed to include all the exculpatory evidence. However, the joint defense team was having trouble finding key evidence because much of the nuance was located within piecemeal chat conversations and complex bid spreadsheets that were buried among millions of similar documents. The joint defense team needed a document search team that was nimble and could quickly identify the most important documents to the defense and share them across the team. They came to Lighthouse because we could quickly identify key documents with accuracy and nuance. How We Did It Lighthouse first organized a central search desk, where all members of the joint defense team could go for document search requests, with results shared across three defense teams. Next, the Lighthouse team located the most important documents related to each of the 15 episodes of price-fixing allegations, on a priority basis. They used linguistic expertise to create narrow searches, taking into consideration the nuance of acronyms, slang, and terminology used within the company and the food manufacturing industry. They also leveraged Lighthouse’s proprietary, cutting-edge search analytic tools to look for key information buried in hundreds of thousands of Excel spreadsheets and chat messages. As the government produced more documents, the Lighthouse team refreshed their searches, looking for key documents in each new production and quickly sharing results across the defense team. As defense preparations continued throughout the year, we we supported all aspects of trial preparation, including two mock trials, all witness preparation binders, and the James hearing. Lighthouse support will continue through the criminal trial for the senior executives, due to our proven success in supporting ad hoc search requests and providing results in real time. The Results The Lighthouse team efficiently delivered incredibly accurate results, saving the underlying client more than $3M thus far. Out of an always-in-flux review population that eventually grew to over 16M documents, Lighthouse was able to cull through the irrelevant data to find and deliver only the most important documents for the defense team’s utilization. In the end, that amounted to less than 1% of the initial review population, including: 4.7K documents for the joint defense group to defend the episodes of alleged price fixing 5.3K documents for defense team’s specific ad hoc and witness kit requests (an average of 400 documents per witness kit) In comparison, a traditional linear review using search terms and conventional analytics performed by multiple case teams typically results in 5-20% of the data population being tagged as “key documents.” This volume would then be funneled to the case teams for review as well, where they would waste valuable time and resources looking at hundreds of thousands of irrelevant or run-of-the-business documents. In addition to cost-efficiency, the team has gained expertise in the key events, timelines, and context of conversations buried within the data. As such, the team is now a critical resource to the defense, supporting all stages of the investigation and assisting in pivot ad hoc requests. Examples include finding a unique pricing document buried among volumes of near duplicates, as well as the relevant context surrounding a single line of a chat message. In the end, Lighthouse saved the underlying company significant time and money that could not have been achieved otherwise. Additionally, our expertise in the data was a critical resource to the joint defense team, which relied on Lighthouse at each step of trial preparation. Lighthouse expert support will continue throughout the criminal trial. ‍ Corporate Case Studyantitrust; ai-and-analytics; ediscovery-reviewantitrust, ai-and-analytics-ediscovery-review, kdi, key document identification

In-house legal and compliance teams use Lighthouse Spectra, a cloud-based, self-service legal technology platform, to achieve a more efficient and scalable approach to compliance monitoring. Our self-service technology keeps clients well ahead of audits and compliance risks, while lowering the costs and inefficiencies inherent to compliance monitoring, particularly for companies working in heavily regulated industries. Clients avoid the processing fees and wait times that burden compliance reviews by quickly and easily loading their own data. Then, they leverage industry leading technology to create repeatable, scalable compliance workflows that quickly cull out irrelevant data and uncover key information. The results are lower risk, faster results, and unprecedented savings. Repeatable and Effective Self-Service Compliance Investigation Workflow Below, we’ve detailed a sample self-service compliance workflow—including real results that our clients have achieved at each step during internal investigations. Similar workflows have been used by our clients to deliver up to 96% reduction in document review and over $800K in savings across a single investigation. Step 1: Automated Data Upload, Processing, and Deduplication What it does : Reduces administration time, speeds up investigation setup, reduces hosting costs, reduces review population by removing duplicates Lighthouse self-service automation features reduce the manual set up tasks that often delay the start of an investigation (data import, processing, etc.). Clients can leverage Lighthouse’s native file managing technology at this point to significantly reduce hosting costs—by only loading native files if or when they’re necessary to the investigation. Once data is uploaded and processed, clients can deploy Lighthouse deduplication technology to immediately remove redundant data. Results : Enabled an investigation team to start analysis one week earlier than standard processing; reduced data population by 25%. Step 2: ECA Culling and Search Term Iteration What it does : Reduces review populations by removing irrelevant documents Once processed and deduplicated, clients use our customized culling and search term iteration processes to swiftly narrow the scope of documents for review. Results : Reduced review population of an internal investigation by over 78%. Step 3: Thread Suppression and Proprietary Review Technology What it does : Reduces review populations by identifying the most unique documents Clients can then implement customized workflows that combine email thread suppression with Lighthouse review technology to identify the most unique documents. Results : Reduced review population of an internal investigation by over 50%. Step 4: Lighthouse TAR and Advanced Analytics What it does : Finds the key documents that matter to investigations. After the culling process, clients often deploy Lighthouse’s Continuous Active Learning TAR workflows to find relevant documents. Once reaching a point of diminishing returns, advanced analytics such as clustering, categorization, and concept searches can be deployed to ensure that no relevant documents were left behind. Results : Reduced review population of an internal investigation by over 60%. Corporate Case Studyspectra; self-service, spectra; compliance-and-investigationsediscovery-review; client-success; ai-and-analyticsCase-Study; client-success; ai-and-analytics; analytics; Compliance-and-Investigations; Corporate; Corporation; data-analytics; eDiscovery; fact-finding; healthcare-investigations; investigations; machine-learning; predictive-coding; Processing; risk-management; self-service, spectra; Spectra; TAR; TAR-Predictive-Coding; technology-assisted-review; edicovery-review; ai-and-analytics

A global transportation company was under investigation for possible infractions of the Foreign Corrupt Practices Act (FCPA) in India. The company’s legal counsel needed to quickly produce responsive documents and find key documents to prepare their defense. Key Results 4M total documents reduced to 250K through 2 rounds of responsive review, with precision rate and recall of 85% or higher. 810 key documents quickly delivered to outside counsel, saving them hours of review and gaining more time for case strategy. A Complex Dataset Requiring Nuanced Approaches The company collected 2M documents from executives in India and the U.S. Information in the documents was extremely sensitive, making it critical to produce only those documents related to the India market. This would be impossible for most TAR tools, which use machine learning and therefore can’t reliably differentiate between conversations about the company’s business in India from discussions solely pertaining to U.S. business. Finding key documents to prepare a defense was challenging as well. The company wanted to learn whether vendors and other third parties had bribed officials in violation of the FCPA, but references to any such violations were sure to be obscure rather than overt. Zeroing In On the Right Conversations Lighthouse used a hybrid approach, supplementing machine learning models with powerful linguistic modeling. First, our linguistic experts created a model to remove documents that merely referred to India but didn’t pertain to business in that market, so that the machine learning TAR wouldn’t pull them into the responsive set. Then our responsive review team developed geographic filters based on documents confirmed as India-specific and used those filters to train the machine learning model. The TAR model created an initial responsive set, which our linguists refined even further with an additional model, based on nuances of English used in communications across different regions of India. By the end, our hybrid approach had reduced the corpus by 97%, with an 87% precision rate and 85% recall. Once this first phase of review was successfully completed, Lighthouse dove into an additional 2M documents collected from custodians located in India. Finding Key Documents Among Obfuscated Communications To help inform a defense, our search specialists focused on language that bad actors outside the company might have used to obfuscate bribery. The team used advanced search techniques to examine how often, and in what context, certain verb-noun pairs indicating an “exchange” were used (for instance, commonly used innocent pairings like give a hand vs. rarer pairs like give reward). The team could then focus on the documents containing language indicating an attempt to conceal or infer. $1.7M Saved, 810 Key Documents Found to Support Defense Lighthouse performed responsive review on two datasets of 2M documents each, reducing them to less than 250K and saving the client more than $1.7M. Out of the 237K responsive documents, Lighthouse uncovered 810 hot docs spanning 7 themes of interest. The work was complete in just 3 weeks and enabled outside counsel to provide the best defense to the underlying company. Corporate Case Studykdi; key-document-identification; case-study; investigations; reviewediscovery-review; client-success; ai-and-analyticsCase-Study; client-success; ai-and-analytics; analytics; document-review; eDiscovery; fact-finding; investigations; KDI; key-document-identification; keyword-search; TAR; TAR-Predictive-Coding; technology-assisted-review; machine-learning; transportation-industry; automotive-industry; edicovery-review; ai-and-analytics

A healthcare provider needed help simplifying ESI hosting for a complex series of 14 related matters across 9 states (and growing). Lighthouse went above and beyond—providing a unified workflow from hosting to review. Key Actions Quickly migrated 11M documents from existing Relativity and non-Relativity databases into a single repository, supported by AI Created one sophisticated workflow—from ESI storage to managed review—for over 14 matters across 9 states (and any other matters that arise in the future) Leveraged advanced technology to facilitate data re-use, data reduction, and review efficiency ‍ Key Results Avoided duplicate collections, hosting, and review of 1.2M documents Instantaneously provided production sets to all 14 matters, giving local counsel time to focus on unique matter documents before production Set case teams up for success in future matters with a readymade data repository, workflow, and trained review team—exponentially increasing the client’s ROI Data Everywhere and No One to Turn To A large healthcare provider was facing a growing number of separate but related litigations. With 14 ongoing matters in 9 different jurisdictions, the company’s data was spread out across multiple ESI vendors and a variety of review databases. The hosting costs of this data sprawl was threatening to explode the company’s overall budget. And with each case team and vendor taking their own approach to case strategy and review, in-house counsel was busy herding cats rather than managing overall litigation strategy. They came to Lighthouse desperately seeking a way to consolidate their overall eDiscovery approach to these matters. A Streamlined Solution for Multiple Matters, from Hosting Through Review Lighthouse seamlessly integrating all related matters into an advanced document repository. Backed by AI, this repository connected insights across matters and maximized work product reuse. Using this repository as a base, our experts built a sophisticated eDiscovery workflow for all 14 individual matters. Each process in every individual matter—from hosting to document review—was purposefully designed around insights and data from all other related matters. The result of this holistic approach was more efficient, consistent, and accurate eDiscovery across every matter—at a much lower cost than could ever have been achieved with a traditional siloed approach. Here’s how we did it: Faster, More Versatile Migration Capabilities With our advanced technology and unique migration expertise, Lighthouse quickly migrated 11M documents from existing databases—including Relativity and non-Relativity—into an advanced AI-backed document repository. At the outset, the team worked closely with the client to understand the scope, types of data, and future needs, so that the migration flowed quickly and efficiently. This approach meant that the client only had to process data once, rather than paying for processing and re-processing data with every matter. Individual case teams also immediately reaped the benefit of data and insights from every related matter, including matters that had already been successfully litigated. This helped counsel anticipate issues in their own matters, while re-using review work product for greater efficiency and consistency—ultimately saving costs and improving matter outcomes. One Hash for Unprecedented Cross-Matter Deduplication and Efficiency Unlike other data storage repositories, the Lighthouse AI-backed repository adds a hash system unique to Lighthouse. This technology normalizes documents before adding a hash value, extending our deduplication power and allowing us to identify all duplicate documents beyond what is possible using traditional deduplication technology. Our unique AI hash system also enabled faster insights into opposing party productions. The Lighthouse team used the system to compare newly received productions in one matter against documents previously received in other matters. Where matches were found, any issue coding one case team applied to a document was carried over and applied to new matching documents. This helped facilitate case team collaboration and a consistent legal strategy across matters. Broad Bench of Data Experts Rather than paying separate vendors for expertise in individual matters, in-house counsel and local case teams leaned on Lighthouse’s unified bench of subject matter experts—including ESI processing and hosting, advanced analytics, and review specialists. These experts worked together as a dedicated client service team, providing a uniquely holistic view of the entire array of related matters. However, individual specialists tagged in to perform work only when their expertise was needed, ensuring that the company didn’t rack up expensive invoices for consulting services they didn’t need or use. When our experts were called in to help, they were able to identify areas for greater efficiency and cross-matter consistency that would have been impossible if the client had remained with a siloed approach to each matter. For example, before review began, Lighthouse review experts counseled individual case to teams to implement a coding layout for each jurisdiction that facilitated work product reuse and consistency across matters. As new related matters come up, our experts will bring their deep institutional knowledge to continue to drive these types of unique efficiency and consistency gains. A Strategic Approach Leads to Faster Reviews and Productions Once data was migrated into the document repository, Lighthouse review experts designed one strategic review plan for all 14 matters that lowered costs and maximized data reuse and cross-matter insights. As part of this plan, Lighthouse created one national review database and separate jurisdiction-specific review databases. Then, Lighthouse experts used advanced AI and review technology to isolate a core set of 150K documents within the 11M documents housed in the repository that were most likely to be responsive across all jurisdictions. This core set was published to the national review database and fully reviewed by an experienced Lighthouse review team trained by our review managers to categorize each document for both national and jurisdictional responsiveness. After review, Lighthouse copied this strategic production set to each jurisdictional database. This approach kept hosting costs drastically lower for each individual matter, while providing all local case teams with an immediate first production, well ahead of production deadlines. Corporate Case Studyai; ai-and-analytics; analytics; artificial-intelligence; big-data; case-study; corporation; corporate; data-analytics; data-re-use; data-reuse; document-review; ediscovery; ediscovery-migration; healthcare-litigation; litigation; managed-review; prism; tar; tar-predictive-coding; technology-assisted-reviewediscovery-review; ai-and-analytics; client-successAI, ai-and-analytics, analytics, artificial-intelligence, Big-Data, Case-Study, Corporation, Corporate, data-analytics, Data-Re-use, Data-Reuse, data-re-use, document-review, eDiscovery, eDiscovery-Migration, healthcare-litigation, litigation, managed-review, Prism, TAR, TAR-Predictive-Coding, technology-assisted-review, ediscovery-review, ai-and-analytics

A large healthcare provider faced a series of related matters requiring document review. Lighthouse designed and executed a single review workflow that provided accurate, consistent, and efficient productions. Lighthouse Managed Review Results Efficient, compliant productions across 14 matters in 9 states (and counting) Nuanced document review performed by one experienced review team, eliminating the need to train multiple review teams Case teams avoided re-reviewing 150K core documents by reusing 100K high-quality review decisions and redactions A Perfect Storm of Review Complexities A large healthcare provider was facing 14 related matters across 9 states. The initial corpus of documents numbered 11M, with each jurisdiction adding more. While each matter shared a core set of relevant issues, they all had their own unique relevancy scope and were being handled by different outside counsel and eDiscovery teams. The corpus was also littered with personally identifiable information (PII) that required identification and redaction by review teams before production. Combining Expertise and Tech to Drive Efficiency The company turned to Lighthouse because of our extensive experience working on complex document review. Our review managers developed a sophisticated workflow to reduce the number of documents requiring review and re-review across jurisdictions by leveraging advanced technology. Custom Workflow Enables Work Product Reuse To lower costs and maximize consistency across matters, Lighthouse created an overall document repository and review database, as well as separate jurisdictional databases. The team migrated all 11M documents into the document repository and used advanced AI and review technology to isolate a core set of documents that were most likely to be responsive across all jurisdictions. Our review managers efficiently worked with all outside counsel teams to validate this core set. They also suggested and implemented a coding layout for each jurisdiction to facilitate work product reuse and consistency across matters. One Skilled Review Team and Review Process for All Matters Our combination of managed review, advanced technology, and custom data re-use workflow resulted in a single document set that met all jurisdiction-specific production requirements. These documents were duplicated across all databases for immediate production in multiple matters. To get to this caliber of review, our review managers used technology to reduce the number of documents needing eyes-on review to 90K and trained an experienced review team on both universal and jurisdictional responsiveness. Technology was also used to expedite PII redaction and propagate coding to the core set of 150K documents. Unprecedented Review Time and Cost Savings With Lighthouse’s review approach, each case team had more freedom in how they structured their post-production workflows. Our approach also provided stricter control of data and enabled more accurate and predictable billing for the client. Further, all 14 matters now had an initial production ready at the push of a button. In addition to lowering costs, this gave local counsel additional time to assess case strategy, with the first production available in advance of agreed-upon deadlines. Instantaneous Initial Production for Multiple Matters Beyond the stellar review outcomes achieved across each matter, Lighthouse’s strategic workflow and use of technology also saved the client an impressive $650K—a delightful surprise to the client, who was prepared to pay more for such a complex litigation series. As new related matters arise, the client can engage a trained and experienced review team ready to hit the ground running. Corporate Case Studycase-study; ai; ai-and-analytics; analytics; artificial-intelligence; big-data; corporation; corporate; data-analytics; data-re-use; data-reuse; document-review; ediscovery; litigation; prism; pii; phi; healthcare; healthcare-litigation; hipaa-phi; managed-review; review; tar-predictive-coding; technology-assisted-review; tar; productionediscovery-review; ai-and-analytics; client-successCase-Study, client-success, AI, ai-and-analytics, analytics, artificial-intelligence, Big-Data, Corporation, Corporate, data-analytics, Data-Re-use, Data-Reuse, data-re-use, document-review, eDiscovery, litigation, Prism, PII, PHI, Healthcare, healthcare-litigation, PII, PHI, HIPAA-PHI, managed-review, document-review, review, TAR-Predictive-Coding, technology-assisted-review, TAR, Production, ediscovery-review, ai-and-analytics

Over the course of five months, Lighthouse delivered approximately 4,500 documents for review—out of the 2.3 million document review set—for a Fortune 100 health insurance provider. The Challenge Complex internal False Claims Act investigation 2.3M total documents for review Five-month timeline and tight budget Lighthouse Key Actions Provided curated weekly deliveries of the most important, inclusive documents for review—with no redundant or duplicative versions Compiled summary reports of each delivery (including highlights of high-priority information) to expedite counsel review Out of 2.3M documents, identified and delivered just the 4,500 documents counsel needed to review in order to conduct a comprehensive legal analysis Key Results for Counsel Immediately gained a grasp on the relevant facts and timelines hidden within a massive review set—without wasting time reviewing irrelevant information Quickly developed a deeper understanding of the underlying risks and nuances of the investigation, through consistent and iterative communication with Lighthouse search experts Confidently completed the investigation on time and within budget—even after large volumes of new data were added mid-investigation A Challenging Internal Investigation into False Claims Act Violations A Fortune 100 health insurance provider was pursuing an internal investigation involving potentially improper diagnosis practices undertaken by a wholly-owned provider group. The scope of the investigation included analysis of reimbursements processed across 20+ disease categories, potentially triggering False Claims Act violations. With 2.3M documents to review, it was unclear how the internal investigation would be completed within a constrained budget and timeline. Counsel reached out to Lighthouse for help. Lighthouse Hands Counsel the Keys to a Focused, Efficient Investigation A small team of Lighthouse information retrieval, legal, data science, and linguistic experts immediately began working with counsel to understand the specific allegations at issue, as well as catalogue the various sources of data that needed to be investigated. The team then designed and executed a battery of complex searches tailored to find instances of fraud or wrongdoing related to the allegations at hand. By staying in close communication with counsel, the Lighthouse team ensured that new search requirements and data sources were quickly integrated into the workstream to support fact development. On a weekly basis, Lighthouse delivered a streamlined set of documents responding to counsel’s evolving theory of the case. These deliveries also included a detailed breakdown of the categories of documents identified each week, descriptions of relevant internal processes and policies, and flagging of high-priority documents of particular interest to counsel. Each delivery was distilled down to only the most inclusive, non-redundant versions of relevant documents. In addition to keeping pace with ongoing requests and deliverables, the Lighthouse team also re-executed previous searches to address waves of new data rolling in midway through the engagement. A Faster and More Comprehensive Investigation Resolution Over the course of five months, Lighthouse delivered approximately 4,500 documents for review—out of the 2.3 million document review set. The Lighthouse deliveries encompassed everything counsel needed to know in order to resolve their investigation—and nothing more. The team accomplished this precision through deep subject matter expertise surrounding the allegations and underlying issues at play, consistent and effective communication with counsel, expert topic-based searching, and additional proprietary data analytics to remove unnecessary duplicative content. By the end of their short engagement with Lighthouse, counsel had developed a comprehensive understanding of the pertinent risk areas and confidently completed their investigation—on time and within budget. Corporate Case Studycase-study; corporate; corporation; ediscovery; fact-finding; document-review; investigations; kdi; key-document-identification; keyword-search; insurance-industry; analytics; ai-and-analyticsediscovery-review; ai-and-analytics; client-successCase-Study, client-success, Corporate, Corporation, eDiscovery, fact-finding, document-review, investigations, KDI, key-document-identification, keyword-search, insurance-industry, analytics, ai-and-analytics, ediscovery-review, ai-and-analytics

Lighthouse experts uncover key evidence in just two weeks eliminating 97% of document set. The Challenge Complex internal investigation into potential employee fraud 627K total documents Two-week timeline Key Results for Counsel Confidently completed a complex fraud investigation in just two weeks—without fear of missing critical information Significantly mitigated risk to the company through the identification of previously unknown internal control gaps Lighthouse Key Actions Executed 22 strategic searches, based on expert analysis, to identify all relevant evidence of employee fraud and misconduct Uncovered hidden information, previously unknown to counsel, that revealed additional acts of fraud, embezzlement, and misconduct by targeted employees—as well as potentially problematic internal control gaps Out of 627K documents, identified and delivered, just the 16K documents counsel needed to review in order to conduct a comprehensive fact investigation A Complex Employee Fraud Investigation The audit division of a health insurance provider was pursuing an internal investigation involving potentially concealed employee conflicts of interest with external vendors. The allegations involved possible defrauding of the parent organization through noncompliant contract and billing practices, as well as embezzlement of membership incentives for personal use and gain. With approximately 627K documents to review on an exceptionally tight timeline of two weeks, it was unclear how a comprehensive internal investigation would be completed to ensure proper due diligence. Counsel reached out to Lighthouse for help. Lighthouse Experts Quickly Uncover Key Evidence A small team of Lighthouse information retrieval, legal, data science, and linguistic experts immediately began working with counsel to understand the specific allegations at issue. As part of this work, the Lighthouse team catalogued the various sources of data that needed to be investigated. Based on counsel’s theory of the case, the team devised eight main search themes that would enable them to find instances of fraud or wrongdoing related to the allegations at hand. Over the course of the short two-week engagement, the Lighthouse team completed 22 discrete searches with corresponding deliveries based on expert analysis of the eight priority search themes. Each delivery was distilled down to include only the most inclusive, non-redundant versions of relevant documents so counsel wasn’t bogged down by reviewing a slew of duplicative and/or irrelevant documents. Over the course of searching, Lighthouse experts quickly uncovered new key information that was previously unknown to counsel. This information revealed a picture of internal control gaps used to circumvent company policies, leading to problematic vendor contract arrangements and suspect billing practices. Separately, the Lighthouse team also uncovered details of relevant personal circumstances of targeted employees. This new information shed light on the potential motivation for bad acts, including substantial personal debt, resentment of parent company controls, and personal relationships with superiors in the management reporting structure. Significant Risk Mitigation and Faster Investigation Resolution with Lighthouse In just two weeks, Lighthouse delivered a targeted set of approximately 16K documents, out of a total 627K in the review set. The Lighthouse deliveries represented everything counsel needed to know about the possible fraudulent employee activity—including concealed information that posed significant risk to the company if it had been left undiscovered. The team was able to accomplish this precision through deep subject matter expertise regarding the fraud allegations, comprehensive metadata analysis and emotional content detection, consistent and effective communication with counsel, expert topic-based searching, and exhaustive content deduplication. With Lighthouse’s partnership, counsel quickly gained a thorough understanding of the internal controls, potential fraud, and the embezzlement issues at play—ultimately enabling them to significantly mitigate risk and complete their investigation in just two weeks. Corporate Case Studycase-study; corporate; corporation; ediscovery; fact-finding; document-review; investigations; kdi; key-document-identification; keyword-search; insurance-industry; analytics; ai-and-analytics; fraud-detectionediscovery-review; ai-and-analytics; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, Corporate, Corporation, eDiscovery, fact-finding, document-review, investigations, KDI, key-document-identification, keyword-search, insurance-industry, analytics, ai-and-analytics, fraud-detection, ediscovery-review, ai-and-analytics

By partnering with Lighthouse, clients reduce their data and save millions of dollars while ensuring quality and security. What They Needed Recently, Lighthouse was brought in by the Department of Justice (DOJ) of a large western US state who had to produce data for a high-stakes, multi-million dollar breach of contract matter. The client was dissatisfied with their current eDiscovery panel and was looking for a new provider who could help centralize eDiscovery with document review, use advanced technologies to reduce data, and ensure quality and security. How We Did It To kick things off, Lighthouse and the client team met to discuss the key goals and expected outcomes of this particular case. It became very clear that the client wanted to reduce data in a defensible way and so our team of legal and technology experts got to work. At the start of the matter, our team collected and processed more than 3.5TBs of client source data (i.e. 9M documents) as well as 98K documents that had been produced by opposing counsel and 135K documents that had been produced by 22 various third parties. In addition, we collected approximately two dozen mobile devices as well as advised and assisted outside counsel on a declaration defending the process for collection and production of mobile devices. Next, we brought in the use of best-in-class technology. We leveraged our search consulting team to apply our early case assessment (ECA) tool to the data after processing, and less than 14% of the original corpus (i.e. 1.2M documents) was promoted from the ECA database. Within the ECA environment, we assisted the client with culling, search term iteration, and helped the client to develop and sample search terms for use during negotiations with opposing counsel. After agreeing upon and validating search terms with opposing counsel, the result set was promoted from ECA for review. Within the review environment, we instituted a technology assisted review (TAR) workflow to reduce the overall review population to 420K documents (a 65% reduction after applying ECA) and prepared defensibility reports for opposing counsel. Finally, we used our thread suppression technology to suppress duplicative emails. ‍ We then developed a custom automated workflow to incorporate confidential de-designation decisions from 16 co-defendants on individual documents and reproduced them. An additional 155K documents were loaded directly to review without culling. For review of the remaining ~500K records, we then implemented our managed review solution—managing a review team (provided by our trusted review partner) through a very successful first pass review, privilege review, and privilege log creation process. ‍ The Results Ultimately, the client produced 260K documents in this matter and saved significant time and money. Lighthouse was able to reduce the original corpus by more than 95% through the use of best-in-class technology and our legal, review, and technology experts. Because of the service quality, support, breadth of capabilities, and expertise exhibited during the matter, the client has since migrated several active matters from different providers to Lighthouse. ‍ Corporate Case Studycase-study; ediscovery; tar; tar-predictive-coding; investigations; analytics; predictive-coding; privilege; privilege-reviewediscovery-review; ai-and-analytics; client-successCase-Study, client-success, eDiscovery, TAR, TAR-Predictive-Coding, investigations, analytics, predictive-coding, privilege, privilege-review, ediscovery-review, ai-and-analytics

Lighthouse partners with a healthcare company, saving $145K in document review costs after reducing review time by 90% through a custom review process. How We Did It Initial Processing Lighthouse used our proprietary processing automation to ingest, load, and deduplicate a total of 690K documents. Our deduplication process was able to immediately achieve a 25% data reduction by removing 175K documents. ECA Culling and Search Term Iteration Results Next, Lighthouse applied our customized culling and search term iteration processes to the 143K eligible documents and families. This process removed 81K documents, reducing the review population by over 55%. Thread Suppression and Proprietary Review Technology Results Lighthouse then implemented a customized workflow that combined email thread suppression with our proprietary review technology to identify the most unique documents. This process removed a total of 31K documents from the review population, thereby reducing the review population by another 50%. Lighthouse TAR and Advanced Analytic Results After the culling process, Lighthouse’s Review & Advanced Analytics team guided counsel through a Continuous Active Learning TAR workflow to find relevant documents. Once we reached a point of diminishing returns, we leveraged advanced analytics such as clustering, categorization, and concept search to ensure that no relevant documents were left behind. Our TAR and advanced analytics removed 17K documents, representing another 50% in data reduction. Corporate Case Studycase-study; corporate; corporation; ediscovery; tar; tar-predictive-coding; ai-and-analytics; analytics; predictive-coding; healthcare-litigation; healthcare; processing; machine-learningediscovery-review; client-successCase-Study, client-success, Corporate, Corporation, eDiscovery, TAR, TAR-Predictive-Coding, ai-and-analytics, -analytics, predictive-coding, healthcare-litigation, Healthcare, Processing, machine-learning, ediscovery-review

Lighthouse's proprietary AI technology solves a unique data deduplication challenge while migrating over 25 terabytes for an extensive healthcare system. Key Results In 5 months, Lighthouse migrated four databases—with 25 TBs of data—all while keeping the databases active for review and production for current matters. Leveraging our AI technology, Lighthouse created an innovative solution for a large volume of Lotus Notes files originally processed as HTML files by a legacy processing tool. This solution ensured that any new Lotus Notes files would deduplicate against the migrated data, regardless of the file type or the tool used for processing. A Challenging Data Deduplication Problem A large healthcare system had been hosting its data (over 25 TBs of data across four databases) on another vendor’s platform for nearly a decade. The company knew it was time to modernize its eDiscovery program with Lighthouse. In order to do so, all 25 TBs would need to be migrated over to Lighthouse for hosting and future processing. However, in addition to data migration, the company also had a unique deduplication challenge due to the previous vendor’s original processing tool. The company’s data had originally been processed with the vendor’s legacy processing tool—which processed Lotus Notes data as HTML files, rather than the more modern EML version. The prior processing of these files into an HTML format meant that whenever duplicate Lotus Notes files were added to the database and processed using a more modern processing tool, those EML files would not deduplicate against the older HTML files in the databases. With over half their data consisting of Lotus Note files processed by the older tool in HTML format, the company was concerned that this issue would significantly increase review cost and slow down review time. Thus, in addition to the overall migration process, the company came to Lighthouse with an unfortunate Catch-22: in order to modernize its processing and eDiscovery capabilities, it was losing the ability to deduplicate a majority of its data with each new ingestion. Lighthouse Migration Expertise Because of the volume of new clients moving to Lighthouse for eDiscovery support, Lighthouse has developed an entire practice group dedicated to data migration. This group is adept at creating customized solutions to the unique challenges that often arise when migrating data out of legacy systems. The team works closely with each client to understand the scope, types of data, challenges, and future needs so that the data migration process is seamless and efficient. The Lighthouse migration team quickly got to work gathering information from the healthcare company to start this process, paying particular attention to the Lotus Notes deduplication issue. Once all relevant information was gathered, Lighthouse worked with stakeholders from the organization to form a comprehensive migration plan that minimized workflow disruption and included a detailed schedule and workflow for future data. In the process, Lighthouse also developed a custom solution for the Lotus Notes issue using our proprietary AI technology. An Innovative Solution: Lighthouse AI Lighthouse’s advanced AI technology can create a unique hash value for all data, no matter how it was originally processed. The Lighthouse migration team leveraged this innovative technology to create a unique hash value for the Lotus Notes files that were originally processed as HTML files. That hash value could then be matched against any new Lotus Notes files that were added to the database by the company, even when those files were processed as EML files. With this proprietary workflow, the healthcare company was able to seamlessly move to Lighthouse’s eDiscovery platform, which was better equipped to serve its eDiscovery needs—without losing the ability to deduplicate its data. Set Up for Success In just five months, Lighthouse completed a seamless migration of the healthcare company’s data by creating a custom migration plan that minimized blackouts and kept all databases up and running. Importantly, Lighthouse also leveraged its proprietary AI to create an innovative solution to a complex problem, ensuring continued deduplication capability and reduced discovery costs. ‍ Corporate Case Studycase-study; ai; ai-and-analytics; ai-big-data; corporate; corporation; ediscovery; ediscovery-migration; prism; processing; project-management; healthcareediscovery-review; ai-and-analytics; client-successCase-Study, client-success, AI, ai-and-analytics, AI-Big-Data, Corporate, Corporation, eDiscovery, eDiscovery-Migration, Prism, Processing, Project-Management, Healthcare, ediscovery-review, ai-and-analytics

Top ten global law firm revitalizes their eDiscovery program with Lighthouse Managed Services for one predictable, recurring price. What They Needed After years of carrying hefty infrastructure costs and operating with limited access to emerging eDiscovery solutions, one of the ten largest law firms globally decided to look for a new eDiscovery partner that could advance their existing eDiscovery program without the burden of unpredictable, piecemeal pricing and sub-par technology. In particular, the firm was interested in a predictable cost model that would provide them with access to forensics, information governance, and eDiscovery experts as well as innovative new analytic and chat technology. To further complicate things, they had less than two months to migrate all of their existing data to the newly selected vendor before they would have to renew payments with their existing vendor. How We Did It Lighthouse Managed Services was a natural fit for this cutting-edge client. We were selected as the firm’s eDiscovery provider because it was clear we could provide a wide-range of subject-matter experts, access to best-in class technology (particularly our proprietary Spectra ® and SmartSeries ™ , as well as third-party tools like Nuix, Relativity, and Brainspace) and deliver within their tight timeline requirements – all for one predictable, recurring price. After the selection process, Lighthouse immediately tackled the migration of over 130 cases and ~13 TB of the firm’s data from their existing vendor’s environment to the Lighthouse environment within the 45-day requirement. Once the cases were restored, we worked with the firm to develop custom workflows that would allow the new data to flow through active migrated matters seamlessly without loss of deduplication, matter-level settings, or work product. We then developed a comprehensive eDiscovery playbook for our client detailing customized, repeatable, and defensible eDiscovery processes for every stage of the EDRM. We also began technology training sessions to allow our client to effectively utilize their access to tools like Relativity and Brainspace, as well as our proprietary Spectra and SmartSeries technology. Further, Lighthouse developed a custom Relativity template to ensure the user experience in Relativity mirrored the law firm’s workflows for continuity. We scheduled bi-weekly meetings with the Lighthouse Product Development team to keep the firm’s team abreast of new features on the horizon as well as allow the firm an opportunity to influence the overall product roadmap. All of this work was completed under a predictable, recurring pricing model, with custom reports around the firm’s matters and metrics. Results Overall, Lighthouse Managed Services surpassed of all the firm’s expectations – completely revitalizing their eDiscovery program for one predictable pricing model. We successfully completed the entire data migration within 45 days, without any disruption to case teams. Once migrated, our client was elated with the access Lighthouse provided to the best technology on the market, as well as the comprehensive training we offered their teams which enabled them to leverage these tools more effectively. In particular, Spectra enabled the firm to administer matters autonomously while getting data into a review platform at a much greater speed than ever before. Since the time of the launch, this client has started over 90 new matters in Spectra, leveraging the analytics, predictive coding, automated redaction, privilege log creation, and chat messaging tools that make our self-service solution the best in its class. Providing all these comprehensive services under a recurring, predictable processing model allowed this client to successfully manage cost recovery and integrate with their client billing seamlessly. Law Firm Case Studycase-study; ediscovery; self-service, spectra; spectra; analytics; processing; managed-review; document-review; review; law-firmediscovery-review; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, eDiscovery, self-service, spectra, Spectra, analytics, Processing, managed-review, document-review, review, Law-Firm, ediscovery-review

Lighthouse collected, processed, and imaged 550 GB of data in less than 96 hours, saving a client from an eight-figure sanction. What They Needed Lighthouse’s client, an Am Law 100 firm, had to respond to a request for production in a highly sensitive matter. The client originally contracted another eDiscovery service provider for collection, processing, and production. Much of the collected data was corrupt and the other service provider was unable to handle a large majority of the data. Facing an eight–figure sanction if the production deadline was missed, the client abandoned their provider and contacted Lighthouse. Lighthouse had 14 days to resolve corrupt data, process the data, identify and segregate the already reviewed data, provide the unreviewed data for review, and produce the responsive data. Complicating matters even further, the data set was sizeable—550GBs—and the client needed at least a week to review the data before production. How We Did It Collect, Analyze, Repair A close inspection of the data revealed that another on-site collection would be necessary in order to deal with the corrupt data. On February 9, two forensic experts from Lighthouse collected three email exchange servers totaling 550 gigabytes. Lighthouse was able to repair some of the corrupt data; however, some data was corrupt at the source. This corrupt data could not interfere with the production to the government so Lighthouse processed the non-corrupt data overnight. The client then requested additional searching and culling for a specific list of custodians. Reduce, Process, Deliver As a result of the way the data was stored, Lighthouse had to navigate through a large number of files to identify the data belonging to the list of custodians. Ultimately, Lighthouse was left with 245 gigabytes which it further culled and filtered. Lighthouse’s experts then segregated 8,000 documents that the client previously reviewed so that the client did not have to waste time re-reviewing these documents. With the deadline looming, Lighthouse immediately imaged the documents for review. Lighthouse provided client with just over 25,000 images for review on February 13. Results As a result of Lighthouse’s speed and ability to handle the corrupt data, the client avoided an eight-figure sanction. In a matter of 96 hours, Lighthouse forensically collected 550 gigabytes from three email exchange servers, extracted 245 gigabytes from those servers, identified 8,000 documents in a corrupted media environment, and imaged over 25,000 documents. Law Firm Case Studybig-data; case-study; collections; ediscovery; forensics; law-firm; processing; production; project-managementediscovery-review; digital forensics; client-successBig-Data, Case-Study, collections, eDiscovery, digital forensics, Law-Firm, Processing, Production, Project-Management, ediscovery-review, digital forensics

A prominent law firm leveraged a cloud-based software solution to increase efficiency and scale, resulting in significant costs savings. What They Needed A mid-sized East Coast law firm­—known for its expertise and experience in complex and high-stakes matters—was looking for new software to replace its in-house legacy technology. Their in-house tool did not provide the level of sophistication or throughput the team needed to continue to scale their work for their clients. In assessing their potential new partner, the firm required access to best-in-class technology, in particular Relativity and Nuix, as the firm’s employees were already familiar with these platforms. In addition, they wanted to leverage automation to have repeatable processes that would save both themselves and their clients time and money. ‍ How We Did It Lighthouse Spectra was selected for its simple and intuitive interface that allows users to internally manage client matters across best-in-class technology – including Relativity, Nuix, and even Brainspace. With Spectra, the firm can now start matters immediately, without having to go through the vendor solicitation and/or statement of work processes, creating real time savings. And the monthly subscription price for Spectra gave them more transparency around billing and greater cost control to help them stay within their budget. The onboarding and training processes were quick, due to the experience of the internal team coupled with the ease of use of Spectra’s. After the initial deployment of Spectra, the firm started processing client data through the tool immediately. They were able to get these matters through processing (Nuix) to review (Relativity) within a few hours, rather than an entire day or more, as was typical with their previous in-house solution. We can go from soup to nuts without having to reinvent the wheel each time. It is truly self-service. — Law Firm The Results Soon after onboarding, the firm took on a couple quick-turn and complex matters that they were able to handle more quickly due to the speed and scale of Spectra, as well as the support of Spectra team. In one instance, they received a request late in the work day that needed to be turned around within a short period of time. Prior to deploying Spectra, that would have taken some hands-on experience and a day’s worth of time. With Spectra, they were able to process it as soon as they received it and it was available for review within a few short hours. In another instance, the firm received a request with a pressing deadline where the document set consisted of approximately 95% foreign-language text. Quickly translating the text to English was imperative to firm’s success. To solve this problem, the Spectra team pointed the firm to a machine language translation tool that easily integrates with Spectra. By deploying the integrated translation service on the workspace, documents submitted for translation were loaded back into the workspace as easily as if it was performing a mass edit. This provided an easy solution for the firm for this particular matter, and now that it’s integrated, the feature is available to the firm on demand. By moving to Spectra, the law firm was able to leverage best-in-class technology, gain more transparency and control around the entire eDiscovery process, and create efficiencies and therefore, reduce costs for themselves and their clients. Leveraging Spectra, the law firm can now do more with less and scale their business to support their clients’ growing needs. ‍ Law Firm Case Studycase-study; ediscovery; self-service, spectra; spectra; ai-and-analytics; analytics; processing; tar-predictive-coding; technology-assisted-review; tar; law-firmediscovery-review; client-successCase-Study, client-success, eDiscovery, self-service, spectra, Spectra, ai-and-analytics, analytics, Processing, TAR-Predictive-Coding, technology-assisted-review, TAR, Law-Firm, ediscovery-review,

With Spectra, Penningtons Manches Cooper accelerated their ediscovery workflow and created a more efficient and cost-effective process. What They Needed Penningtons Manches Cooper LLP, a leading UK and international law firm, was looking for an innovative solution to enable more efficient and cost-effective management of their increasing eDiscovery needs. At the same time, they also wanted a solution that would not require a large investment in hardware, additional personnel, or training. When the team at Penningtons Manches Cooper reached out to Lighthouse about their needs, we suggested Spectra, our cloud-based, self-service eDiscovery solution that would enable the team at Penningtons Manches Cooper to easily run their matters in a more efficient and predictable manner. Additionally, the team at Penningtons Manches Cooper also wanted the option of leveraging experienced, knowledgeable, and on-demand assistance as required. Because Spectra offers the ability to seamlessly transition a matter from self-service to Lighthouse’s full-service team of eDiscovery experts, it provided the Penningtons Manches Cooper team a level of reassurance that there would always be help on hand should it be needed. The team at Penningtons Manches Cooper agreed that Spectra was the right eDiscovery solution for them. How They Did It Penningtons Manches Cooper partnered with Lighthouse to deploy Spectra, which was implemented within three months from initial proof of concept to rollout with live matters. Primary areas of focus during the implementation were training, process design, and internal change management. The project began with roundtable sessions to fully understand the scope and ensure that deployment was customized to fit Penningtons Manches Cooper’s requirements, deliverables, and goals. And because Spectra is a cloud-based solution, there was no capital expenditure or additional IT resourcing required for implementation. This allowed for a flexible approach, fast implementation, and low ongoing maintenance for Penningtons Manches Cooper. Once the tool was initially implemented, the team at Penningtons Manches Cooper identified a suitable matter to be used in a proof of concept. Lighthouse trained key Penningtons Manches Cooper personnel on how to use Spectra, and together the two teams worked to create a scalable and repeatable workflow for particular work types. All items were recorded in a bespoke playbook, which fully documents Spectra’s capabilities and process as well as specific Penningtons Manches Cooper requirements. Next, Lighthouse provided training to the wider Penningtons Manches Cooper team on Spectra, Brainspace, and Lighthouse’s proprietary SmartSeries® tools to enable the firm to leverage automated redaction, chat, and other emerging solutions. Due to the simplicity and on-demand nature of Spectra, the team at Penningtons Manches Cooper was able to realize a 1 to 4-hour reduction in the time it takes to create a matter and upload data into Relativity. Further, Lighthouse developed a custom Relativity template. to ensure the user experience in Relativity is mirrored across matters and complements the firm’s workflows. Following the successful trial period, Penningtons Manches Cooper has identified and managed many other matters in Spectra with very little external support. Setup of each new matter has been reduced significantly, in some circumstances by up to 2-3 days, as there has been a significant reduction in the number of steps required to instruct external eDiscovery vendors, including no need to gather price proposals, no delay while vendors run conflict checks, and no need for any additional contract negotiation. As a consequence, each legal team was typically able to begin reviewing documents on the same day the data was received by the firm. In conjunction with the above, predictable and recurring billing practices were implemented and custom reports were developed around the firm’s matters and metrics. This, in turn, will allow Penningtons Manches Cooper to manage cost recovery and integrate billing for a more seamless and efficient process. The Results Penningtons Manches Cooper partnered with Lighthouse to roll out Spectra, which enabled their team to control the process from the very start and create efficiency and predictability of cost and process. By using Spectra, the team at Penningtons Manches Cooper was able to create matters in Relativity and Brainspace as well as upload and process data quickly, all within a simplified and intuitive interface. The use of best-in-class technology, combined with repeatable process and in-house expertise, created a tangible benefit, ensuring eDiscovery and document review are completed with minimal cost, a savings which can be passed on directly to the client. Law Firm Case Studycase-study; financial-services-industry; corporate; corporation; ediscovery; self-service, spectra; spectra; analyticsediscovery-review; client-successCase-Study, client-success, financial-services-industry, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, analytics, ediscovery-review

Lighthouse Spectra helps a considerable healthcare organization gain control, pricing transparency, and efficiency gains in the eDiscovery process. What They Needed A large healthcare organization was looking to solve their eDiscovery challenges around speed and cost. Specifically, they needed to increase their overall efficiency, and have more control over their matters with truly transparent and lower ediscovery-related costs. How We Did It Lighthouse Spectra was chosen to help achieve these key goals. Spectra is a self-service, on-demand eDiscovery tool with a transparent subscription-based pricing model. Spectra users can also access a full-time project management team at Lighthouse, whenever needed – all for one predictable price. Spectra onboarding was tailored to the users’ needs and focused on teaching users how to use Spectra itself, as well as when and how to use Brainspace, an analytics engine available inside the platform. Since Spectra is built with an intuitive interface, it only took a few short trainings over the course of a few weeks for the users to become comfortable using it. The Lighthouse team also ensured that Relativity and Spectra were customized to the organization’s specific needs. Our teams ensured that all customized permissions and views were set up within Relativity and worked with the organization to create custom Relativity templates to apply their standard coding pallets, rule-based coding propagations, pre-baked saved searches, standard views/layouts, imaging profiles, and more. Additionally, the Lighthouse team also assisted in building a continuous multi-model learning (CMML) workflow for their team to leverage within Spectra. Once set up was complete, the organization immediately started leveraging Spectra to process their data and run search terms as needed on a variety of diverse case types, including labor and employment cases, internal investigations, and OIG requests. The Results By moving to Spectra, the healthcare organization gained more control over their eDiscovery processes, created more efficient workflows, and achieved significant cost savings with transparent and predictable pricing. Since deploying the tool, the organization found that using the search and analytics capabilities of Spectra reduced the volume of natives to just 4.5% of the total hosted volume, minimizing the count of documents being reviewed by 95%. The custom Relativity template prevents the need to reinvent the wheel with each new matter and drive consistency across their portfolio. Further, the CMML workflow allows the organization to prioritize review of documents that are most likely to be responsive, as well as minimize the number of documents that go to review. Both of these enhancements allowed the organization to increase their overall speed from collection to production while lowering their overall eDiscovery-related costs. Through these new workflows and processes, the healthcare organization has achieved both defensibility and affordability and reduced review time from days to hours. This has resulted in an overall savings of $500K in their first year with Spectra.\ Corporate Case Studycase-study; corporate; corporation; ediscovery; self-service, spectra; spectra; ai-and-analytics; analytics; processing; tar-predictive-coding; technology-assisted-review; tar; healthcareediscovery-review; client-successCase-Study, Corporate, Corporation, eDiscovery, self-service, spectra, Spectra, ai-and-analytics, analytics, Processing, TAR-Predictive-Coding, technology-assisted-review, TAR, Healthcare, ediscovery-review

Lighthouse partners with a global law firm to meet a 60-day production deadline for an 11.5 million-document population, saving the firm millions. What They Needed A global law firm was representing a large analytics company being investigated by the Federal Trade Commission (FTC) for antitrust activity. The company faced an extremely aggressive production deadline—approximately 60 days to collect, review, and produce responsive documents from an initial data population of roughly 11.5M. How We Did It The firm partnered with Lighthouse to create a workflow to execute multiple work streams simultaneously (collections, processing, TAR, privilege review, and logging) to ensure the company could meet the production deadline. Lighthouse expert teams managed the entire process, implementing daily standup calls and facilitating communication between all stakeholders to ensure that each workflow was executed correctly and on time. Lighthouse clients that leverage our AI technology to its full potential can realize even more cost savings and efficiency. For example, in this case, this global law firm would have seen the removal of close to 420K documents from privilege review that our AI accurately (as verified in the qc process) deemed to be highly unlikely or unlikely to be privilege. The Lighthouse team also provided strategic and defensible review methods to attack data volume and increase overall efficiency throughout the project. This included Technology Assisted Review (TAR) and email thread suppression in combination with our proprietary AI-technology and privilege log application. The different work streams that Lighthouse designed and executed to reduce the time, burden, and expense of review included: Lighthouse Forensic Collection : Lighthouse’s dedicated expert forensic team implemented a workflow to perform all initial collections, as well as all refresh collections across M365 mailboxes, Teams data, OneDrive, and SharePoint. TAR 1.0 : Lighthouse implemented predictive coding via a TAR 1.0 workflow to systematically find and remove non-relevant documents in a defensible manner. Not relevant documents that fell below the cutoff score were removed from the review population to reduce privilege review. Non-TAR Review : A detailed file analysis was conducted on documents that could not be scored via the TAR model by Lighthouse experts to remove non-responsive documents from eyes-on responsiveness review. Email Threading : Once TAR 1.0 reached stability and a cutoff score was achieved, Lighthouse applied email thread suppression on the documents above the cutoff score to further decrease privilege review and the production set overall. Managing Teams data : The Lighthouse team leveraged our proprietary chat tool to deduplicate Microsoft Teams data. Using the tool, the team stitched Teams messages back together in a format that allowed outside counsel to easily see the conversation in totality (e.g., who was part of the thread, who entered/left the chat room, who said what, at what time, etc.). The tool then integrated and threaded chat messages with search and filtering capabilities for review directly in Relativity. Privilege Review : Even as collections, TAR 1.0, email threading, and document review workflows were ongoing, the Lighthouse advanced analytics team leveraged technology in combination with their expertise to drastically reduce the privilege review set and guard against inadvertent production of privileged documents: Lighthouse Strategic Privilege Reduction : Lighthouse data reduction experts worked with outside counsel to analyze the data to identify large categories of documents that could be safely removed from privilege review, such as two large tranches of calendar items that were pulled into the privilege review. Lighthouse also ran a separate header-only privilege screen across and located a pattern in the privilege hits, which outside counsel confirmed were not privileged and removed from privilege review. AI-enabled Privilege QC : To minimize risk and increase efficiency of privilege review, Lighthouse deployed our advanced AI-technology, which uses multiple algorithms to analyze the text and metadata of documents, enabling highly accurate privilege predictions. First, it analyzed the entire review workspace and identified additional privileged documents that were not picked up by the conventional privileged screen approach. Then, the tool was utilized in privilege review QC workflows where it helped reviewers overturn first and second level privilege calls. Privilege logging application : Lighthouse also leveraged our privilege logging application to automate privilege log generation, saving outside counsel significant time and driving consistent work product in creating their privilege log. The Results Lighthouse forensic collection collected roughly 11.5M documents from more than 600 unique datasets and over 90 custodians, spanning M365 mailboxes, Teams data, OneDrive, and SharePoint sources. Lighthouse’s TAR 1.0 workflow then dramatically reduced the document population for privilege review, ultimately removing over 6M documents in full families from review, thereby delivering a savings of nearly $6.2M. The Lighthouse team’s detailed file analysis of non-TAR universe resulted in an additional 640K files removed from responsiveness review—encompassing close to a 90% reduction in the non-TAR review volume and delivering a savings of roughly $640K. Our email thread suppression process then removed another 1.1M documents from review (for a savings of $1.1M), while the Lighthouse proprietary chat tool removed over 63K Teams items and generated over 200K coherent transcript families from 1.3M individual messages. Law Firm Case Studycase-study; antitrust; ediscovery; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; ai-and-analytics; ai-big-data; artificial-intelligence; ai; acquisitions; analytics; predictive-coding; prism; privilege; privilege-review; name-normalization; microsoft; emerging-data-sources; forensics; collectionsediscovery-review; ai-and-analytics; antitrust; chat-and-collaboration-data; client-successCase-Study, client-success, Antitrust, eDiscovery, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, ai-and-analytics, AI-Big-Data, artificial-intelligence, AI, Acquisitions, analytics, predictive-coding, Prism, privilege, privilege-review, name-normalization, microsoft, Emerging-Data-Sources, digital forensics, collections, ediscovery-review, ai-and-analytics, antitrust, chat-and-collaboration-data

Cleary Gottlieb and Lighthouse save millions of dollars and thousands of hours in HSRs Second Request for Fortune 500 company. What They Needed A global Fortune 500 electronics company received an HSR Second Request from the Department of Justice (DOJ), with an extremely aggressive timeline to reach substantial compliance. They engaged Cleary Gottlieb (“Cleary”), a global technology-savvy and innovative law firm with extensive experience handling challenging Second Requests. After Cleary led negotiations with the DOJ to reduce the scope of the investigation, the client was faced with 3.3M documents to review—a significant subset of which included CJK language documents that would require expensive and time-consuming translation. To further complicate matters, the DOJ and Cleary remained engaged in ongoing scope negotiations, resulting in additional data being added throughout the project. Cleary knew that conventional TAR technology was not capable of evaluating a dataset with ever-changing review parameters. How Cleary and Lighthouse Did It CJ Mahoney, counsel and head of the eDiscovery and litigation technology group at Cleary, has extensive experience working on complex HSR Second Requests and has pioneered a number of different analytics-driven methods to reach substantial compliance in the past. Based on prior joint success in innovating new ways to use this technology to improve privilege analytics, CJ immediately saw the potential of Lighthouse’s proprietary AI technology for this challenge. Together, CJ and the Lighthouse data scientists developed a unique training workflow to achieve highly precise responsive prediction results on this challenging dataset. CJ secured the DOJ’s first-ever approval of this workflow with Lighthouse’s proprietary AI technology. Immediately after approval, responsive and privilege analysis and review began simultaneously, enabled by AI technology. For responsiveness, the teams utilized an active learning TAR workflow wherein subject matter experts reviewed a control set of randomly selected documents. After only a few training rounds, the system reached stability and began scoring the remaining dataset for responsiveness. A privilege classifier was built based on 20K previously confirmed privilege calls and applied to score all documents in the privilege workspace. The teams used a combination of the analytic results and privilege terms to identify potential privileged documents. All documents within this set that were scored as “highly likely to be privileged” were immediately routed to reviewers for review and privilege logging. Conversely, documents scored as “unlikely to be privileged” were removed from privilege review after Cleary’s attorneys verified the accuracy of the results using a random sample. Further, the teams used the privilege classifier to identify additional privilege documents that had not hit on privilege terms. As the timeline for substantial compliance approached, negotiations with DOJ regarding relevant timeframes and custodians continued, resulting in the near-constant addition and removal of documents from the dataset. The Lighthouse and Cleary teams managed the ever-changing dataset with ease using the Lighthouse technology and workflow developed by the teams. The Results Using a specialized TAR workflow leveraging advanced AI, the teams delivered highly accurate responsive classification, resulting in more than 500K (or more than 40%) fewer documents requiring further review and production to the DOJ, when compared to legacy TAR tools. By creating a smaller volume of documents requiring production, the amount of privilege and foreign language review was also lessened. For example, 120K fewer foreign language documents were included in the final responsive set compared to legacy TAR tool results. This reduction of review and translation saved approximately $1M alone. For the client, the smaller responsive set meant faster production turnaround times, lower overall costs, and risk mitigation through the decreased chance for inadvertent production of non-responsive documents. The Lighthouse and Cleary partnership resulted in the removal of 200K documents from privilege review beyond what could have been possible through conventional methods, leading to cost savings of $1.2M and time savings of 8K review hours. The team further mitigated risk to the client by identifying privilege documents that did not hit on standard privilege terms. The Cleary and Lighthouse partnership resulted in substantial compliance with the HSR Second Request, increased risk mitigation, faster document review, and remarkable savings for the client. Law Firm Case Studycase-study; antitrust; ediscovery; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; ai-and-analytics; ai-big-data; artificial-intelligence; ai; acquisitions; analytics; predictive-coding; prism; privilege; privilege-review; tech-industryediscovery-review; antitrust; ai-and-analytics; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, Antitrust, eDiscovery, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, ai-and-analytics, AI-Big-Data, artificial-intelligence, AI, Acquisitions, analytics, predictive-coding, Prism, privilege, privilege-review, tech-industry, ediscovery-review, antitrust, ai-and-analytics

A global pharmaceutical company leverages Lighthouse's AI-powered analytics to reduce legal spending, increase efficiency, and decrease risk in their matters. Driving Value on Individual Matters The pharmaceutical company first came to Lighthouse for better, faster review for a single matter. Leveraging our unparalleled range of advanced analytics accelerators, our experienced review managers and expert consultants created a custom review workflow that significantly reduced data volume, expedited review, and increased the accuracy of data classification. Individual Matter Review Workflow and Metrics Driving Value Across All Matters Based on the results from the first matter and Lighthouse’s ability to attain even more review efficiency by connecting matters, the company sent additional matters to Lighthouse. Applying advanced AI across the company’s matters resulted in deeper matter insights and upleveled the accuracy of classification models in ways that that would be impossible on one single matter. As each new matter is added, Lighthouse AI identifies data that overlaps with past and concurrent matters. This has two impacts at the outset: 1) significant processing cost savings and unprecedented 2) early insights into new matters. These insights empower counsel to make more strategic, data-backed decisions from the start, leading to extraordinary downstream efficiencies and significantly reduced risk. For example, across five currently connected matters for the company, Lighthouse AI showed that: “Outside Counsel A” email domains were coded privileged over 95% of the time. Emails with a government email domain on the communication were coded privilege 15% of the time. 20K documents of Custodian B were collected and processed across multiple matters, but only 10 documents were ever actually reviewed. Custodian C’s documents were reviewed and produced across multiple matters, with a 0% privilege rate. Lighthouse AI-powered insights and connections supercharge the efficiency, accuracy, and consistency for each subsequent matter. Past attorney work product and metadata are used to reduce the need for eyes-on review and improve the consistency and accuracy of review for responsiveness, privilege, PII, confidentiality, redactions, and more. Driving Value into The Future The efficiency and risk mitigation benefits continue to grow for the pharmaceutical company with each new matter. A true big data technology, the more data Lighthouse advanced analytics ingests, the deeper and more nuanced its decision-making and insights become. Opportunities for data and attorney work product re-use will also grow with each new matter ingested, amplifying the company’s ROI into the future. Corporate Case Studycase-study; ai; ai-and-analytics; analytics; artificial-intelligence; big-data; corporation; corporate; data-analytics; data-re-use; data-reuse; document-review; ediscovery; litigation; prism; privilege; privilege-review; pii; phi; pharmaediscovery-review; ai-and-analytics; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, AI, ai-and-analytics, analytics, artificial-intelligence, Big-Data, Corporation, Corporate, data-analytics, Data-Re-use, Data-Reuse, data-re-use, document-review, eDiscovery, litigation, Prism, privilege, privilege-review, PII, PHI, Pharma, ediscovery-review, ai-and-analytics

Lighthouse helps global British bank resolve critical risks during a major technology overhaul. Key Actions Microsoft referred Company to Lighthouse to address eDiscovery needs within Microsoft 365 (M365) Lighthouse assembled a team whose members had former expertise gained from stakeholder departments that were affected by the unresolved needs Key Results Compliance risks were successfully remediated using native M365 tools The Company used its new platform to avoid the need for add-on services or vendors What They Needed M365 Implementation Yields Data Risk Management As one of the nation’s largest financial institutions, the Company’s move to M365 required exceptional time and care—further complicating compliance requirements for record-keeping, data protection, and regulated conduct, and ultimately placing demands on M365 that created uncertainty of whether the platform could be resolved. The complex compliance requirements fueled an internal audit, revealing several risks related to the Company’s management of unstructured data, including its practices for retention, deletion, preservation, and protection of sensitive information. The Company asked Microsoft for help—and Microsoft referred the Company to Lighthouse. Tight Deadlines, Exceptional Solutions Lighthouse was tasked to explore whether M365’s native information governance (IG) and eDiscovery tools could address the risks identified in the audit. The team launched a series of workshops, interviews, and research tasks to: Educate stakeholders about M365’s native capabilities for records and information management (RIM) and IG Define stakeholders’ needs and current workflows regarding RIM and IG Analyze gaps in the current state Test and propose new workflows using native M365 tools Executives intensely monitored this project, as every identified risk was critical, so the pressure on the teams’ proposed workflows was tremendous—not to mention a tight 12-week timeline. Lighthouse prevailed, fielding a team of experienced peers with the Company stakeholders. Every business group—from records management to IT that were responsible for remediating risks—was paired with a Lighthouse consultant who had previously filled a similar role at a comparable institution. Our experts gained rapid credibility with each stakeholder group, and they ultimately accomplished a unified solution that was acceptable to all parties. Our solution succeeded in remediating all flagged risks using RIM and IG workflows within M365. It required the Company to upgrade its M365 licensing agreement from E3 to E5, but the company agreed that the added cost was more than worth it. In the end, Lighthouse achieved two key wins: 1) demonstrating to the Company that M365 could meet even the most stringent security and compliance needs, and 2) securing a new trusted partnership with the customer that has continued to develop. ‍ Corporate Case Studycase-study; big-data; cloud-migration; cloud; cloud-services; corporate; corporation; emerging-data-sources; information-governance; ediscovery; microsoft; legacy-data-remediation; risk-management; record-management; financial-services-industrymicrosoft-365; information-governance; client-success; lighting-the-path-to-better-information-governanceCase-Study, client-success, Big-Data, Cloud-Migration, cloud, Cloud-Services, Corporate, Corporation, Emerging-Data-Sources, Information-Governance, eDiscovery, microsoft, Legacy-Data-Remediation, microsoft, risk-management, Record-Management, financial-services-industry, microsoft-365, information-governance

Lighthouse saves insurance giant millions of dollars during major technology upgrade. Key Actions Microsoft referred the Company to Lighthouse to resolve existing concerns from the Company’s IT and legal departments that were stifling their automation and transition process to Microsoft 365 (M365). Lighthouse held educational workshops on eDiscovery tools within M365, and devised a comprehensive plan for the compliance. Key Results Unblocked the M365 transition effort and enhanced the partnership between legal and IT. Compliance concerns were answered within M365, saving the company millions of dollars in retaining or updating legacy data management systems. What They Needed Legal Concerns Churn 11th Hour Nightmare for IT Department In 2017, a nationwide insurance giant initiated a transition from an on-premises Microsoft solution to a cloud-based M365 solution fueled by gain from cost, performance, and security improvements. Years later, and well past the intended launch date, the Company’s legal team suddenly halted the transition entirely due to concerns of M365’s eDiscovery capabilities, specifically, how M365 would handle the identification, preservation, and collection of email, instant messages, and files for the Company. The legal department insisted the company retain its custom-built archival solution until all compliance concerns were allayed. These demands put the IT department in an extremely tough spot after having already invested several years into the transition to M365. If forced to extend their aging, on-premises solution, the team would face substantial costs. To help unstick the implementation project, Microsoft suggested the Company engage Lighthouse to assist. Lighthouse immediately understood the legal team’s concerns and acted swiftly to address the Company’s insistence on exercising the transition to M365 with great caution, all while remaining vigilant of the Company’s receipt of hundreds of new legal matters monthly. The sensitive nature of data in this industry and the complex regulatory environment made the potential risk related to mismanagement very high. The process was intricate and complex, and required high-level integration to mitigate the significant risks that were specific to individual privacy regulations, such as the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR). Hands-on Experience and High-touch Service Bridge the Gaps Lighthouse fielded a team of experts with direct experience in the same or similar roles as the various client stakeholders, ranging from IT to records management, corporate legal, and public affairs. This hand-selected team led a three-part process with their counterparts from the Company: Providing education on the eDiscovery aspects of M365 Analyzing current workflows and performance, and expressing their desired future state Devising a high-level design document for how relevant parties could conduct eDiscovery tasks in compliance with the requirements while using M365 The first two processes helped restore unity among stakeholders, while the design document delivered on the legal team’s concerns, including specified settings for a range of M365 applications and components, such as Exchange Online, SharePoint Online, OneDrive for Business, and Teams. The design document made room for process automation and/or custom workflows, as well as for third-party system integration (for compliance archive, legal hold, matter management, etc.). The initial project success led to a continuing relationship between the Company and Lighthouse, and over time Lighthouse has become a critical element in the Company’s ongoing M365 implementation and adoption journey helping them in charting a path forward. Corporate Case Studycase-study; big-data; cloud-migration; cloud; cloud-services; ccpa; corporate; corporation; data-privacy; data-protection; emerging-data-sources; information-governance; ediscovery; microsoft; gdpr; legacy-data-remediation; legal-holds; risk-management; insurance-industry; record-managementmicrosoft-365; data-privacy; information-governance; client-success; lighting-the-path-to-better-information-governanceCase-Study, Big-Data, Cloud-Migration, cloud, Cloud-Services, ccpa, Corporate, Corporation, Data-Privacy, data-protection, Emerging-Data-Sources, Information-Governance, eDiscovery, microsoft, gdpr, Legacy-Data-Remediation, Legal-Holds, microsoft, risk-management, insurance-industry, Record-Management, microsoft-365, data-privacy, information-governance

Lighthouse bridges internal gaps during technology overhaul and solves longstanding compliance issues for a German multinational healthcare manufacturer. Key Actions Lighthouse engaged company stakeholders in operational planning and received funding from Microsoft to devise and integrate a premium Microsoft 365 (M365) add-on to existing Purview Premium eDiscovery, which resolved an outstanding compliance need. Key Results The proof-of-concept achieved a zero-trust security model integrated with third-party software, and satisfied the barring of critical needs for the Company that centralized IT and legal departments after years of dysfunction. What They Needed Automating a transition to M365 commonly yields a clash between IT, legal, and compliance stakeholders if the decision to convert was spearheaded by IT and made without consulting legal and compliance teams. Typically, during planning or implementation of converting to M365, legal teams ask IT how the new platform will manage compliant and defensible processes, and if IT doesn’t have the answers, the project stalls. This was the situation facing a multinational manufacturing Company that engaged Lighthouse for help during the spring of 2020. At that time, the Company was several years into its M365 transition, and the legal teams’ requirements for adoption of native M365 compliance tools barred a complete transition. Pressure to adopt the tools escalated as M365 workloads for content creation, collaboration, and communication were already rolled out, creating an increasingly large and complex volume of data with significant degrees of risk. Lighthouse Responds to Need and Launches New Technology In partnership with Microsoft Consulting Services, Lighthouse organized a companywide M365 “reset,” hosting a three-day workshop to revamp the transition process and generate an official statement of work. The strategic goal was to streamline the stakeholders from litigation, technical infrastructure, cybersecurity, and forensics teams that previously failed to align. The workshop fielded critical topics geared to encourage constructive discussions between stakeholders and to strengthen departmental trust. The outcome of these discussions eventually enabled the company to move forward with critical compliance updates, including the collection and parsing of Microsoft Teams data, and the management of myriad files and email attachments. Lighthouse took stock of the current state, testing potential solutions, and arrived at a proof-of-concept for an eDiscovery Automation Solution (EAS) that augmented existing M365 capabilities to meet the legal team’s security requirements and remediate any performance gaps. Microsoft recognized the potential value of the EAS for the wider market, ultimately leading to Microsoft funding for the proof-of-concept. Inside the eDiscovery Automation Solution (EAS) Technology Azure-native web application designed to orchestrate the eDiscovery operations of an M365 subscriber through Purview Premium eDiscovery automation Maximized Microsoft Graph API “/Compliance/eDiscovery/” functions and other Microsoft API Simplified to Azure AD trust boundary, targeting the M365 tenant hosted within, and enabling full governance of identity and entitlement throughout Azure and M365 security features Benefits Achieved a zero-trust security model Authorized high-velocity, high-volume eDiscovery tasks without outside technology through automation and orchestration of existing M365 eDiscovery premium capabilities native to M365 Mobilized integration with third-party software included in the Company’s eDiscovery workflows Amplified workload visibility by automatically surfacing relevant Mailboxes, OneDrives, and other M365 group-based technologies dependent upon selected Custodians’ access Corporate Case Studybig-data; case-study; cloud-migration; cloud; cloud-services; cloud-security; corporate; corporation; data-privacy; emerging-data-sources; information-governance; ediscovery; microsoft; manufacturing-industry; risk-managementchat-and-collaboration-data; ediscovery-review; microsoft-365; data-privacy; information-governance; client-success; lighting-the-path-to-better-information-governanceBig-Data, Case-Study, Cloud-Migration, cloud, Cloud-Services, Cloud-Security, Corporate, Corporation, Data-Privacy, Emerging-Data-Sources, Information-Governance, eDiscovery, microsoft, manufacturing-industry, risk-management, chat-and-collaboration-data, ediscovery-review, microsoft-365, data-privacy, information-governance

Lighthouse's forensics experts found hidden clues missed during an internal investigation, proving a departing employee was stealing company data. Lighthouse Key Results By quickly engaging Lighthouse forensics experts: The company stopped proprietary and sensitive information from being disseminated and used by competitors. The company’s law firm was able to quickly take action against the employee, preventing any further malfeasance or damage. Investigation Overview Week 1 Day 1 – 4 — Employee uploads company data onto a personal Google Drive account over the span of four days. ‍ Day 4 – 5 — An internal investigation concludes that all company data has been deleted from the employee’s personal data sources and no further action is needed. However, the company’s outside counsel calls in Lighthouse forensics experts to perform a separate investigation for affirmation. ‍ Day 6 — Lighthouse forensics experts find evidence missed during the company’s internal investigation, indicating that the laptop provided to internal investigators was a “decoy,” and that the employee had actually transferred the proprietary company data onto an as-of-yet undisclosed laptop. Week 2–4 Outside counsel uses Lighthouse’s findings to file a restraining order against the employee and elicit a confession wherein the employee admitted they had downloaded the proprietary data onto a secret laptop—owned by another business. Week 6 Lighthouse forensics team is provided access to the additional laptop and the employee’s private Google Drive account. Although there is no company data stored on the drive, the Lighthouse team dives deeper and immediately finds that the employee had restored the previously deleted company data back to their Google Drive account, transferred it the secret laptop, and then deleted it again from the Google Drive account. These findings enable outside counsel to take additional remediating actions. Suspicious Activity by a Departing Employee Raises Alarm Bells During routine internal departing employee analysis, a global company was alerted to the fact that an employee had uploaded more than 10K files containing sensitive proprietary data to a personal Google Drive account. The company immediately launched an internal investigation and engaged their outside counsel. Over the course of the internal investigation, the employee admitted they had uploaded company data to their Google Drive, and then used an external hard drive to transfer that data onto a personal laptop. However, the employee avowed that all company data had since been deleted—which the company’s IT team confirmed by examining all three data sources. However, due to the sensitivity of the data, outside counsel wanted additional reassurance that the employee was no longer concealing proprietary company data. The law firm had previously relied on Lighthouse forensics experts for similar investigations and knew that they could count on Lighthouse expertise to find any hidden clues that would point to additional hidden data. Finding the Forensic Breadcrumbs Week 1 The Lighthouse forensics team received access to forensic images of the employee’s personal laptop and external hard drive within one week of the first suspicious upload. The team immediately noticed that the employee’s data tracks conflicted with the timelines and statements provided by the employee during the company’s internal investigation. Key Evidence Found by Lighthouse Forensics Experts The external hard drive used to transfer company data had not been plugged in to the personal laptop during the relevant time frame. File paths identified on the external hard drive (which show the file locations where data was downloaded upon connection) did not match those on the personal laptop provided to internal investigators. This evidence led the Lighthouse team to conclude that the laptop provided by the employee was not the laptop used to download company data—and that a different laptop with the stored proprietary company data existed but had not been disclosed by the employee. Week 2–4 A Lighthouse forensics expert provided a sworn declaration explaining the evidence found during the examination of the employee’s personal devices. The company’s law firm used this declaration to file a restraining order to stop the employee from continuing to steal or disseminate proprietary data. The law firm also used Lighthouse’s findings to elicit a confession from the employee, admitting that they had been secretly working part-time for another business, and had transferred the company’s proprietary data onto a laptop provided to the employee by that business. Week 6 Within two weeks of the Lighthouse forensics expert’s sworn declaration, the Lighthouse team was provided access to the laptop owned by the other business, as well as the employee’s personal Google Drive account. Lighthouse’s inspection of the Google Drive did show that all company data had been deleted, as had been confirmed by internal investigators. However, Lighthouse immediately went deeper into the Google Drive and found conclusive evidence that the employee had subsequently “restored” the deleted proprietary data just a few days after the internal investigation ended, in an attempt to continue with the data theft. Key Evidence Found by Lighthouse Forensics Experts Despite the fact that no company data was stored on the employee’s personal Google Drive account at the time Lighthouse received access to it, Lighthouse forensics experts went above and beyond to do a deeper forensic dive into the user activity log, email account, and internet searches stored on the Google Drive. That deeper analysis showed that: Two days after the internal investigation ended, the employee began conducting numerous internet searches for ways to “restore” deleted files on Google Drive. Two weeks later, the employee emailed a private IT company asking for help restoring deleted Google Drive files. One day after sending that email, thousands of files were restored to the employee’s Google Drive. Those restored files were once again deleted a few days later. Before the restored files were re-deleted, the employee downloaded some of the files containing company data to the “secret” laptop owned by another business. Keeping a Lid on Pandora’s Box The evidence found by Lighthouse forensics experts after their initial examination of the employee’s personal devices enabled the company’s law firm to take legal action against the employee less than one month after the first suspicious data upload. Within one day of being provided access to the employee’s personal Google Drive account, Lighthouse forensics experts were able to find exactly how and where the stolen proprietary and sensitive data was hidden. This enabled the company to permanently prevent any dissemination of that proprietary and sensitive data to competitors. ‍ ‍ Corporate Case Studycase-study; corporate; corporation; g-suite; forensics; investigations; collections; fraud-detection; red-flag-reporting; departing-onboarding-employeedigital forensics; client-successCase-Study, client-success, Corporate, Corporation, -G-Suite, digital forensics, investigations, collections, fraud-detection, Red-Flag-Reporting, Departing-Onboarding-Employee, digital forensics

Lighthouse experts distilled crucial information from millions of produced documents for a client's legal strategy during a Department of Justice investigation. Key Actions Lighthouse created 35 deposition kits by conducting two large-scale data investigations—and addressing multiple ad-hoc emergency investigations in the process—on an initial production set of six million documents, identifying the 4,100 most relevant items. Lighthouse adhered to a complex delivery schedule so the case team had time to prepare for each deposition. ‍ Key Results Counsel was well-prepared for 35 depositions using the deposition kits delivered by Lighthouse. Instead of spending time and review cycles finding they evidence, they used the bandwidth they saved to hone their legal strategy. ‍ Responding to a Fast-Moving Government Investigation, with a Merger on the Line When two of the largest publishing companies in the country entered a merger deal, the Department of Justice (DOJ) reacted with a large anti-trust investigation. Pursuant to an HSR Second Request, the companies produced a combined six million documents to the DOJ. In response, the DOJ sought to depose 35 individuals within a few months’ time. This left outside counsel with just two months to prepare for the defense of a massive potential merger, including intensive preparation for all 35 depositions. To do so, they knew they would need to find every shred of relevant information hidden within those six million documents—as quickly as possible. Executing a Plan for Better Legal Strategy When the law firm reached out to Lighthouse for help, our agile search team of analytic, legal, and linguistic experts immediately got to work, consulting with counsel to understand the specifics of the investigation, as well as the case team’s initial strategy for response. Using this background, the Lighthouse team mapped out a information search plan leveraging advanced volume reduction technologies and linguistic search models, delivering: Comprehensive deposition kits for all 35 deponents. Each kit was scheduled to be delivered well ahead of the corresponding deposition date, and included summaries of Lighthouse experts’ findings and highlights of notable documents and facts, in order to give counsel adequate time to prepare for each deposition. Key and relevant documents related to the DOJ’s anti-trust concerns and outside counsel’s defense strategies. These documents, provided on a rolling timeline, were uncovered by conducting two large scale data investigations: one to find all documents related to determining which publishers participated in or won the auctions, and another to find all documents necessary to facilitate the creation of an all-encompassing book auction timeline. Given the legal and analytic expertise of our specialists, Lighthouse search results often uncovered new areas of importance for the case team. When the case team responded to this new information with urgent follow-up search requests (with results sometimes needed in 24 – 48 hours), our team also boosted efforts to provide the requested information. Powering Counsel with Knowledge—and Time By partnering with Lighthouse, the case team stayed focused on preparing for depositions and crafting a response to the DOJ’s concerns to the merger, instead of conducting database searches and reviewing irrelevant or redundant documents. In just two months, Lighthouse found and delivered the 4,100 documents the case team needed, out of an initial population of six million documents. This included creation and delivery of 35 deposition preparation kits, all documents related to the case team’s strategy for responding to the DOJ’s antitrust concerns (delivered on a rolling basis), and results of six ad hoc case team investigation requests. All deposition kit and derivative search deliveries met or exceeded counsel’s delivery deadline expectations. Law Firm Case Studycase-study; document-review; ediscovery; fact-finding; kdi; key-document-identification; law-firm; hsr-second-requests; investigations; mergers; acquisitionsediscovery-review; ai-and-analytics; antitrust; client-successCase-Study, client-success, document-review, eDiscovery, fact-finding, KDI, key-document-identification, Law-Firm, HSR-Second-Requests, investigations, Mergers, Acquisitions, ediscovery-review, ai-and-analytics, antitrust

Lighthouse applies language models and human expertise to uncover critical evidence. What We Did Outside counsel for a large construction firm partnered with Lighthouse to identify key documents Lighthouse used its proven iterative process to reduce the review set Collaborative approach continuously incorporated counsel’s insights into model results Key Results 92,000 documents reduced to 871 Key handwritten reports identified using metadata Counsel freed to focus on most important documents Review completed within the 3-week deadline Piecing Together Contract History Without a Guide A large construction company facing a breach-of-contract suit retained outside counsel. Because personnel involved in the contract were no longer employed by the contractor, the law firm needed to reconstruct the agreement’s history based on related documents and communications. However, with just three weeks for review, a keyword search returned more than 90,000 items. The firm needed a way to identify the most critical documents rapidly and accurately. Iterating and Adapting to Unearth Critical Information The Lighthouse team applied advanced technology and review expertise to get the job done. Counsel provided Lighthouse with 15 topics relevant to contractual changes, such as cost, delays, and weather conditions. The team identified an initial set of documents using linguistic modeling. The law firm provided feedback to update the search models. The insights of the experienced attorneys directed the investigation, while Lighthouse people and technology accelerated the discovery of relevant information. As new topic areas emerged, Lighthouse adapted. They identified additional contractors involved in the dispute and concerns such as employee discontent and time-keeping accuracy. As the search proceeded, they captured important documents even though they were outside the original search parameters. Most importantly, Lighthouse used metadata to highlight relevant site incident reports, the contents of which were not searchable. The law firm could review salient reports in depth, discovering key information concerning the disputed contract. Ensuring Response Readiness Over four iterations, Lighthouse escalated 871 key documents related to 16 case themes, in addition to the handwritten incident reports. Lighthouse data retrieval experts highlighted key language in Relativity and coded and prioritized critical documents to expedite review. Using a powerful combination of linguistic models and case experience, Lighthouse shrank the unwieldy dataset to a manageable size and brought the most critical information to the forefront. Counsel could focus their resources on the most relevant data and maximize value for their client. By the end of the third week and final delivery, the attorneys were well-prepared for negotiations and litigation. Law Firm Case Studycase-study; document-review; ediscovery; fact-finding; kdi; key-document-identification; law-firm; ai-and-analytics; analyticsediscovery-review; ai-and-analytics; client-success; lighting-the-path-to-better-ediscoveryCase-Study, client-success, document-review, eDiscovery, fact-finding, KDI, key-document-identification, Law-Firm, ai-and-analytics, analytics, ediscovery-review, ai-and-analytics

Lighthouse proprietary, technology-enabled strategy for finding key documents gives counsel a strategic advantage in a challenging HSR Second Request. Key Results In just three weeks, the Lighthouse team found the 1K most important documents out of an initial data population of 19M documents. Lighthouse experts began flowing key documents to the case team just three days after the initial kickoff meeting. Lighthouse saved counsel at least a month’s worth of preparation time for witness interviews and defense planning by efficiently finding the most important documents. A Mountain of Data and a Short Timeline A global technology company and their two outside counsel teams needed to quickly prepare a winning defense in a high-stakes, time-sensitive, Department of Justice (DOJ) Hart-Scott-Rodino (HSR) Second Request. To do so, they would have to identify and review all potentially damaging (or alternatively, helpful) documents within an initial data population of 19M documents. Finding the most important documents within that massive data volume—in less than one month—presented a Herculean task. A Proprietary Solution for Finding the Most Important Documents Lighthouse’s technology-enabled search strategy is led by information retrieval experts with decades of industry experience, who utilize robust search technologies that support large data volumes beyond industry-standard tools. Together, this combination of cutting-edge technology and data expertise quickly surfaces critical documents, streamlining legal analysis and case preparation for case teams. Handing Over the Keys to a Strategic Defense With no time to lose, Lighthouse TAR and review experts were able to whittle down the 19M documents to just over 990K responsive documents for production to meet substantial compliance. Simultaneously, Lighthouse experts quickly got to work finding the most important documents for the case team. Rather than relying on keyword culling, the Lighthouse team analyzed the data population and leveraged proprietary algorithms to safely reduce the universe to documents that contained the unique content the case team needed. From there, a team of six data retrieval experts leveraged proprietary search technology and institutional knowledge of the client’s data, gleaned from working with the company in a managed services capacity, to find key documents that were critical to the case team. Our experts used an iterative process and had weekly meetings with the case team so that they could instantly integrate counsel and witness feedback throughout the project, which helped yield more accurate search results. With this process, the Lighthouse team began flowing key documents to the case team just three days after the initial kickoff meeting. Over the course of the next three weeks, the Lighthouse team provided a total 1K key documents (out of a 990K responsive documents) in eight rolling deliveries. By gaining immediate access to these documents and eliminating the need for time-consuming and costly manual review, Lighthouse saved the team at least a month’s worth of preparation time for witness interviews and defense preparation. Law Firm Case Studyantitrust; case-study; document-review; ediscovery; fact-finding; kdi; key-document-identification; tar; tar-predictive-coding; law-firm; hsr-second-requests; investigations; mergers; acquisitionsediscovery-review; ai-and-analytics; antitrust; client-successAntitrust, Case-Study, document-review, eDiscovery, fact-finding, KDI, key-document-identification, TAR, TAR-Predictive-Coding, Law-Firm, HSR-Second-Requests, investigations, Mergers, Acquisitions, ediscovery-review, ai-and-analytics, antitrust

Lighthouse partners with a rapidly expanding pharmaceutical company to streamline its eDiscovery workflow and meet obligations more efficiently. What They Needed A large pharmaceutical client received subpoenas from several regulators. The subpoenas covered multiple product lines, implicated 60 custodians, and virtually all the company’s email. The client’s IT group identified over 35TBs of data requiring collection, processing, and review. Complicating matters further, the company had only 60 days to respond, well outside its estimated time of nine months to complete the project. Faced with this near impossible timeline, the client looked to Lighthouse for support. How We Did It Relying on procedures outlined in a jointly developed eDiscovery Playbook, Lighthouse’s data collection and forensics experts worked closely with the client’s legal and IT groups to implement a defensible strategy that greatly reduced the amount of data requiring collection. Experts from Lighthouse’s Advisory Services group worked with the client to implement a legal hold and data retention policy, customized to the various subpoenas. Lighthouse provided a unified review database, allowing outside counsel (who was responding to separate subpoenas) to leverage each other’s work product, greatly reducing review costs and preventing the inadvertent production of privileged and other sensitive materials. The Results Our combined efforts reduced the originally estimated 35TBs of data requiring review to less than 3TBs. By greatly reducing the amount of data requiring processing and review, the client saved significant review costs and reduced the estimated project completion time from nine months to only four weeks. Review cost reductions were achieved by leveraging Lighthouse’s project management team as well as the company’s proprietary suite of technology-assisted review offerings. These, and other efficiencies discovered during the project, have been implemented in future matters, continuing to drive down costs and increase value. Corporate Case Studyadvisory-services; big-data; case-study; collections; corporate; corporation; ediscovery; forensics; information-governance; investigations; pharma; privilege; privilege-review; processing; project-management; tar; tar-predictive-coding; technology-assisted-reviewediscovery-review; digital forensics; ai-and-analytics; information-governance; client-successAdvisory-Services, Big-Data, Case-Study, collections, Corporate, Corporation, eDiscovery, digital forensics, Information-Governance, investigations, Pharma, privilege, privilege-review, Processing, Project-Management, TAR, TAR-Predictive-Coding, technology-assisted-review, ediscovery-review, digital forensics, ai-and-analytics, information-governance

Lighthouse red flag report prevents proprietary data from being taken by departing employee. Key Actions A global company partnered with Lighthouse to create a proactive departing employee program to prevent data loss and theft. Lighthouse forensics experts prepared Red Flag Reports for every departing employee that fell within a specific category of employees. Each report outlined the risks associated with the departing employee based on a skilled forensic examination of their activity and data. Soon after implementing the program, a Lighthouse Red Flag Report alerted the company to suspicious activity by a departing employee indicating a high risk for data loss. Key Results Because of Lighthouse’s analysis and quick response, the company was able to: Prevent sensitive data from being disseminated outside the company. Avoid costly litigation associated with proprietary data loss. Reevaluate the departing employee’s severance package due to breach of contract, resulting in additional cost savings. ‍ What They Needed A global company was dealing with an increased risk of data loss and theft from departing employees. The company retains large volumes of proprietary data spread across their entire data landscape. Much of that data is also highly sensitive and would create a competitive disadvantage for the company if it were to end up in competitors’ hands. The company was also facing a higher volume of employee turnover—especially within roles that had access to the company’s most sensitive data (e.g., company executive and management roles). The company was concerned that these factors were creating a perfect storm for data theft and loss. They realized they needed a better system to catch instances of proprietary data loss before any data left the company. Company stakeholders reached out to Lighthouse because they knew our forensics team could help them build a proactive, repeatable solution for analyzing and reporting on departing employee activity. How We Did It Lighthouse forensics experts worked with the company to create a custom departing employee program for data loss prevention. With this program, Lighthouse experts prepared a Red Flag Report for every departing employee that fell within specified high-risk categories (e.g., employees above a specific seniority level, or employees that had access to highly sensitive company data, etc.). Each Red Flag Report was prepared by a Lighthouse forensics expert and summarized the data theft risk associated with the underlying employee. Every report contained: A high-level summary of the risk of data theft presented by the employee. A collection of attachments with highlights and comments by the Lighthouse forensics examiner (for example, a list of files stored in an employee’s personal cloud storage account, with an explanation of why that activity may indicate a higher risk of data theft). A forensic artifact categorization with associated risk ratings (e.g., if there were no suspicious search terms found during a scan of the employee’s Google search history, the examiner assigned that category a lower risk rating of “1”). Recommended next steps, with options for substantiating high-risk employee behavior. Reports were delivered to a cross-functional group of company stakeholders, including IT, human resources, and legal groups. The Results The Lighthouse program very quickly paid off for the company. Soon after initiation, Lighthouse escalated a Red Flag Report for a departing employee that showed a high risk of data loss. Specifically, the Lighthouse forensics examiner flagged that the employee had connected two different external thumb drives containing sensitive company data to their laptop. This activity was flagged by the Lighthouse forensics examiner as high risk because: The employee had already been directed by the company to return any device that had corporate data saved on it; and The employee had previously indicated that they didn’t have any devices to return. As soon as Lighthouse escalated the Red Flag Report, company stakeholders scheduled an interview with the employee. This interview resulted in the employee admitting that they had taken corporate data with them, via the two thumb drives. Because Lighthouse was able to quickly flag the employee’s suspicious activity, the company was able to retrieve the thumb drives before the proprietary data was disseminated to a competitor. The company was also able to reevaluate the employee’s severance package due to the breach of company policy, resulting in a significant cost saving. Even more importantly, the company now has a proven, proactive, and customized solution for preventing data loss and theft by departing employees—implemented by Lighthouse’s highly skilled forensics team. ‍ Corporate Case Studycase-study; corporate; corporation; forensics; investigations; collections; fraud-detection; red-flag-reporting; departing-onboarding-employeedigital forensics; client-successCase-Study, client-success, Corporate, Corporation, digital forensics, investigations, collections, fraud-detection, Red-Flag-Reporting, Departing-Onboarding-Employee, digital forensics

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