Lighthouse Blog

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

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

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

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

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

Artificial intelligence (AI), advanced analytics, and machine learning are no longer new to the eDiscovery field. While the legal industry admittedly trends towards caution in its embrace of new technology, the ever-growing surge of data is forcing most legal professionals to accept that basic machine learning and AI are becoming necessary eDiscovery tools.However, the constant evolution and improvement of legal tech bestow an excellent opportunity to the forward-thinking eDiscovery legal professional who seeks to triumph over the growing inefficiencies and ballooning costs of older technology and workflow models. Below, we’ll provide you with arguments to pull from your quiver when you need to convince Luddites that leveraging the most advanced AI and analytics solutions can give your organization or law firm a competitive and financial advantage, while also reducing risk.Argument 1: “We already use analytical and AI technology like Technology Assisted Review (TAR) when necessary. Why bring on another AI/analytical tool?”Solutions like TAR and other in-case analytical tools remain worthwhile for specific use cases (for example, standalone cases with massive amounts of data, short deadlines, and static data sets). However, more advanced analytical technology can now be used to provide incredible insight into a wider variety of cases or even across multiple matters. For example, newer solutions now have the ability to analyze previous attorney work product across a company’s entire legal portfolio, giving legal teams unprecedented insight into institutional challenges like identifying attorney-client privilege, trade secret information, and irrelevant junk data that gets pulled into cases and re-reviewed time and time again. This gives legal teams the ability to make better decisions about how to review documents on new matters.Additionally, new technology has become more powerful, with the ability to run multiple algorithms and search within metadata, where older tools could only use single algorithms to search text alone. This means that newer tools are more effective and efficient at identifying critical information such as privileged communications, confidential information, or protected personal information. In short, printing out roadmap directions was advanced and useful at the time, but we’ve all moved on to more efficient and reliable methods of finding our way.Argument 2: “I don’t understand this technology, so I won’t use it” This is one of the easiest arguments to overcome. A good eDiscovery solution provider can offer a myriad of options to help users understand and leverage the advances in analytics and AI to achieve the best possible results. Whether you want to take a hands-off approach and have a team of experts show you what is possible (“Here are a million documents. Show me all the documents that are very likely to be privileged by next week”), or you want to really dive into the technology yourself (“Show me how to use this tool so that I can delve into the privilege rate of every custodian across multiple matters in order to effectuate a better overall privilege review strategy”), a quality solution provider should be able to accommodate. Look for providers that offer training and have the ability to clearly explain how these new technologies work and how they will improve legal outcomes. Your provider should have a dedicated team of analytics experts with the credentials and hands-on experience to quell any technology fears. Argument 3: “This technology will be too expensive.”Again, this one should be a simple argument to overcome. The efficiencies that the effective use of AI and analytics achieve can far outweigh the cost to use it. Look for a solution provider that offers a variety of predictable pricing structures, like per gig pricing, flat fee, fees generated by case, fees generated across multiple cases, or subscription-based fees. Before presenting your desired solution to stakeholders, draft your battle plan by preparing a comparison of your favored pricing structure vs. the cost of performing a linear review with a traditional pricing structure (say, $1 per doc). Also, be sure to identify and outline any efficiencies a more advanced analytical tool can provide in future cases (for example, the ability to analyze and re-use past attorney work product). Finally, when battling against risk-averse stakeholders, come armed with a cost/benefit analysis outlining all of the ways in which newer AI can mitigate risk, such as by enabling more accurate and consistent work product, case over case.ai-and-analyticsanalytics, ai-big-data, blog, ai-and-analytics,analytics; ai-big-data; bloglighthouse

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

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