Lighthouse Blog

For years, general counsel have weighed the pros and cons of doing a task internally versus sending the work to outside counsel – this is not a new dichotomy. What is newer, however, is the proliferation of technology available for legal and the business savvy now being applied to internal legal departments. This has opened up more choices for legal departments. First, you have to figure out whether you can apply technology, then whether you should build or buy that technology, and finally if you should outsource any portion of the process.Before you start down the path of buy vs. build vs. outsource, I would recommend assessing your department’s offerings. In the earlier parts of this series, I outline how you can do that. Once you understand your services and your gaps, you can better determine where you may need to apply build vs. buy decisions. Whether you are a general counsel or a legal operations professional, this blog will outline four key aspects to include in your framework as you make these decisions.1. Problem/Solution ListStart with a list of services your company needs and possible solutions. If you followed the productization process, you will have a good list. If you have not yet done this, you can at least jot down a list of your company’s legal needs, how pervasive and urgent they are, whether they further the company strategy, as well as any potential solutions.Next, order that list from most pervasive to least pervasive. Where there is a tie, look to the problem’s relationship to company strategy.Next, work through all of the items in box A. You want to be able to answer the following questions:Is there an existing solution?Is there a software solution that may apply?What are the costs/benefits of all possible solutions?Is there typically urgency around the request?All other things being equal, do we have the expertise to handle this in house?If you have gaps in A, B, or C, I would recommend addressing those before process improvement items.2. Cost-Benefit AnalysisNext, for any change (either addressing a gap or a process improvement) you should do a cost-benefit/return on investment analysis. Note that if you are just trying to get a sense of which problem on your list to address, you can do a high-level analysis by categorizing the solutions into low, medium, or high financial impact. If, however, you are getting to the point of suggesting a change internally and asking for budget, you want to do a much more in-depth quantitative analysis. On the benefit side, you want to consider any revenue acceleration for the company (e.g., customers’ revenue hits a quarter earlier) as well as costs reduced and avoided (e.g. outside counsel fees). If there are other quantifiable benefits, you should include them as well. On the expense side, make sure to consider licensing, annual maintenance, user fees, implementation, infrastructure, training, hourly support/expert charges, and any ongoing costs. You should predict these benefits and costs for the next 3 years, as that is a common period to see whether there is a return on your investment. You can also prepare a version of this document showing the same cost/benefit of building the solution internally as well as outsourcing it to outside counsel.3. Additional Factors: Urgency and ExpertiseOnce you have the cost-benefit analysis for the various solutions, you usually have a preferred direction. However, don’t forget to account for time and expertise. You should then consider how urgent the requests are. The more urgent a request, the more likely it should be handled by technology or outsourced, as those solutions typically can bring more resources to bear. You should then consider expertise. More specifically, does one need specific knowledge about the company to solve this problem or will there be a lot of need to liaise internally? If so, the solution should likely stay with the internal corporate legal department. Conversely, does this require niche expertise and is it better handled by an outside counsel with that expertise? Make notes of these considerations with your cost-benefit analysis, as these factors can sway a decision in one direction or another.4. Decision TimeUltimately, making these decisions is more of an art than a science. They are also decisions that can and should be revisited as things change in your business and legal department. The above should give you the right information to make an informed decision. Ultimately, you will want to share your decision with others and get input before finalizing a direction.By following the productization process, orienting your solutions towards your customers, streamlining how you deliver services, and applying the right sets of resources through build versus buy decisions, your legal department will operate more efficiently. legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

In my last two blogs, I discussed how your legal department can productize services to become more efficient as well as shared some tips for how to determine the legal needs within your organization. Now that you know the added benefits and understand the legal needs, the natural next step is to determine what legal service “products” to offer, as well as any gaps. However, if nobody knows what these repeatable solutions are, what good are they? This is where creating an internal marketing plan to get the word out about your department’s legal services is critically important. In this blog, we’ll talk about how to do that by answering who, what, when, where, and why.Who?When you create your internal plan, the first thing you need to do is understand who you are marketing to. The easiest way to do this is to create some simple “personas.” You can easily do this based on the interviews you conducted as part of your earlier search. You should build a persona for each distinct type of user coming to you – typically this aligns with internal departments. In detailing each persona, you should include the following:Typical day-to-day work of your personaTypical interaction with legalTop of mind issues/challengesOther notesWhat?Next, you will need to decide what you are going to market to these personas (i.e repeatable workflows). Common ones in the legal arena are contract, litigation, HR investigation, and patent workflows. Once you have the workflows applicable to your company identified, detail the features of each workflow. For example, it is automated; has six common template documents, a clause library, and contract status; and leverages existing company technology.Once you have your personas, workflows, and features, you’re ready to create a positioning document. You should create one document for every problem/solution set (i.e. workflow). This will form the basis of how you share the information with others. The goal of this document is to position your solution in a way that resonates with the internal users. Below is a format that I find helpful to follow and I have inserted an example based on a contract workflow.PROBLEM: There is a problem in the company today. Contract negotiations are long, cumbersome, and not transparent. This can delay revenue opportunities. In addition, final contracts are difficult to locate and manage.SOLUTION: The ideal solution to this problem is an easy-to-use process, with some contracts being able to avoid legal review. The solution would allow easy access to status for interested parties and would allow those, or other, interested parties to access the contractual information at a later date.PRIMARY MESSAGE (SHORT - 1 SENTENCE): The Corporate Legal Department delivers a business-driven model for negotiating and managing contracts that accelerates, not hinders, company growth.SERVICE DESCRIPTION (2-3 SENTENCES): By leveraging an intake form, employees are directed to a self-service, spectra portal for template contracts or put in touch with an attorney for more complex matters. The status of their request, as well as information about all finalized contracts, is displayed in our JIRA system giving users full access to contract status as well as important contractual data of finalized contracts.HIGHLIGHTS (THESE SHOULD BE PROBLEM-ORIENTED FEATURES):Reduces contract turnaround by leveraging templated contracts and clausesAllows users access to contract status anytime, anywhereNo new systems (i.e. leverages existing company tools)Etc.The above will create a lot of different worksheets and information. Since I like to keep things a little simpler, I also create a cliff notes version of this to show the all-up view of your corporate legal department’s services.Once you have completed your positioning, don’t be afraid to run the messaging by some of the people you interviewed. You want to make sure that it is clear how legal will be helping them get their work done. I would suggest selecting people who are friendly to your department and who you have a good working relationship with since you are running draft information by them and not a final product.Where, When, and Why?Third, you need to think about where, when, and why you are getting the message out. The goal is to get it out wherever your users are, often, and in a way that they like to consume the information. At a minimum, I would suggest doing a launch of the updated services and including information about that launch on:The company wiki page/internal siteAny internal ticketing toolA company newsletter (or a company meeting if appropriate)Any onboarding materials/presentations your company does for new hiresOr even a “roadshow,” where you present to each department within your organization what services the legal team offersDuring any presentation, it is always helpful to inject some fun into the presentation. I have heard of some legal departments doing humorous videos or skits to capture the attention of their employees. Partner with your internal marketing team, as they may have some great suggestions on how you can get the word out.Finally, don’t forget about post-launch messaging. Though you may see an uptick in users after a launch, some people will have missed the information the first time around or will have forgotten it by the time they get to an issue that they want to bring to legal. To that end, make sure you have a plan for continued marketing. I like to showcase successes in follow-up marketing (e.g. a contract turnaround case study showing the reduced times or some metrics on impact). This information can be shared in an employee newsletter or as a quick email to leaders asking them to share it in their department meetings.This is quite a robust process and you should expect it will take several weeks, or even months, to complete. You will also likely continue to refine this marketing plan as you address gaps by adding services and gathering feedback. The benefit of going through this process is that it brings clarity to what legal does, brings efficiency by advertising repeatable workflows, and gives everyone in legal visibility into the challenges in the business and how legal addresses those.legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

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

Many law departments are reactionary. Someone comes to legal with a “legal” question and they help that person. Although this makes a lot of sense, as legal is a support department, it makes it very difficult to thematically explain the value legal is driving as well as understand the work the department is doing. As legal operations matures and legal departments look to be more efficient, productizing the services in the department is a natural progression. This approach was a central discussion at the 2021 CLOC conference and the subject of this blog series. In order to productize something effectively, however, you need a very good understanding of your customer and prospective customers’ needs. In this article, I will give you an overview of how to get that.A central theme in product management is building resonators – products that resonate with the buyers. You may have the best idea but, if it doesn’t meet a pervasive market need, nobody will buy it. There are many great examples of products that failed and dozens of lessons we can learn from those failures. Most of the lessons come back to misunderstanding the customer's need and the nature of that need. For example, people may say they want a better mousetrap but if you don’t ask how much they would pay for that mousetrap, whether they would replace any current mousetraps with a better one, and whether it matters if the new mousetrap gives off an odor of chemicals, you can see how you might not make a best seller. To give an example in the legal services space, in my first general counsel role, I heard from many people how it was frustrating that they could never find contracts when they needed them. I immediately set upon a mission to create a contracts database. After investing a lot of time, we had a wonderfully organized database, and the only person who ever used it was the legal team. So what happened to all the frustrated employees from other departments? It turns out I didn’t ask them how often they needed to look up contracts and whether that need was part of another legal request (meaning that legal was the one actually looking up the contract anyway). In the end, the contract database was extremely helpful for the legal department but I could have saved myself the time of making it self-service, spectra and figuring out permissions for different users had I asked some questions upfront. To avoid the same fate, there are four principles you can use when asking your company about its legal needs.1. Don’t rely on the users to define the needs. Instead, be curious about their day-to-day and in that curiosity, you will be able to see the legal needs. The theory is this: if you ask someone what they need from legal, they will overlay their belief system about what legal should provide before they answer. Instead, when you ask them about their role, their goals, how they are measured, and what their biggest challenges are, you are more likely to be able to understand them and see where legal may be able to help.2. Create a template interview form and use it religiously with each person.When you do 10-15 interviews, you want to be able to discern themes and compare interviews. When multiple people are conducting interviews, you want to be sure you are all hitting the same topics. This is much easier to do when you start from a template. For a 30-minute interview, I would suggest 3-5 template questions. Always get background information before the interview starts including their name, title, department, and contact information. Put this information at the top of your interview summary. Do not include this in your 3-5 questions. Having this information clearly labeled and available allows you to easily follow up later. Next, move on to background and devote 2-3 questions to this area including what are their main goals for the year, how is their department measured, what are their biggest pain points. Finally, go on to any specific areas you may want to ask about. For example, you may want to know how they have used the legal department in the past, how much they interact with overseas colleagues, etc. Here is a list of common questions:What are your department’s goals for the year?How is your department measured?What are your biggest roadblocks in achieving your goals?What are your biggest roadblocks in getting your job done?If you had a magic wand and could change one thing about your job, what would it be?What are your most common needs outside your department?What is your perception of what the legal department does?What kinds of things have you come to legal for?3. Interview a diverse group. It may seem obvious that you need a good sample size, however, you will be surprised at how varied the needs are at different levels and across different departments. If you are only interviewing one person to represent a specific level or department, you should ask them “how representative do you think your pain points/goals are of the department?” This will give you a good idea of whether you can rely on this person’s interview as representative of the department or whether you will have to do some follow-up interviews with others.4. Always ask follow-up questions.The guidance for limiting your template to 3-5 questions above ensures you have time for follow up on each response. More specifically, you want to be sure you are really understanding the responses and quantifying the level and frequency of any relevant pain points. I would set a goal to ask 2 follow-up questions for every first response. For example, if your first question is “what are your goals for 2021?” then you should expect to ask 2 follow-up questions after your interviewee responds. If at any point the person you are interviewing mentions a challenge that you think legal can help to solve, this is your queue to follow up around the pain and pervasiveness. Here are some questions you can ask to get into how big a problem they are facing:How often do you run into this roadblock: daily, weekly, monthly, quarterly?When you run into this roadblock, how much time do you spend resolving it: 1-2 hours, 2-5 hours, 5-10 hours, 10+ hours?Does this roadblock impact multiple people? If so, how many?Does this roadblock (or a stoppage in you moving towards your goals) impact other departments?Are there workarounds for this roadblock? If so, how cumbersome are they on a scale of 1-5?If you had to reach out to another department and work with someone to remove this roadblock each time it came up, would you do that or would you continue with the workaround?How long would you wait for an outside resource to help before you proceed with your current workaround?Does the challenge have an impact on revenue?Whether you are a general counsel just getting to know your organization, a legal operations professional tasked with making your department more efficient, or a lawyer who is interested in ensuring you are providing great services, the above should give you a good place to start to understand your customer. Once you understand your customer, you’re able to provide great resonating services and position your existing solutions. legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

We live in the era of Big Data. The exponential pace of technological development continues to generate immense amounts of digital information that can be analyzed, sorted, and utilized in previously impossible ways. In this world of artificial intelligence (AI), machine learning, and other advanced technologies, questions of privacy, government regulations, and compliance have taken on a new prominence across industries of all kinds.With this in mind, H5 recently convened a panel of experts to discuss the latest compliance challenges that organizations are facing today, as well as ways that AI can be used to address those challenges. Some key topics covered in the discussion included:Understanding use cases involving technical approaches to data classification.Exploring emerging data classification methods and approach.Setting expectations within your organization for the deployment of AI technology.Keeping an AI solution compliant.Preventing introducing bias into your AI models.The panel included Timia Moore, strategic risk assessment manager for Wells Fargo; Kimberly Pack, associate general counsel of compliance for Anheuser-Busch; Alex Lakatos, partner at Mayer Brown; and Eric Pender, engagement manager at H5; The conversation was moderated by Doug Austin, editor of the eDiscovery Today blog.Compliance Challenges Organizations Are Facing TodayThe rapidly evolving regulatory landscape, vastly increased data volumes and sources, and stringent new privacy laws present unique new challenges to today’s businesses. Whereas in the recent past it may have seemed liked regulatory bodies were often in a defensive position, forced to play catch-up as powerful new technologies took the field, these agencies are increasingly using their own tech to go on the offensive.This is particularly true in the banking industry and broader financial sector. “With the advent of fintech and technology like AI, regulators are moving from this reactive mode into a more proactive mode,” said Timia Moore, strategic risk assessment manager for Wells Fargo. But the trend is not limited to banking and finance. “It’s not industry specific,” she said. “I think regulators are really looking to be more proactive and figure out how to identify and assess issues, because ultimately they’re concerned about the consumer, which all of our companies are and should be as well.”Indeed, growing demand by consumers for increased privacy and better protection of their personal data is a key driver of new regulations around the world, including the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) and various similar laws in the United States. It’s also one of the biggest compliance challenges facing organizations today, as cyber attacks are now faster, more aggressive, and more sophisticated than ever before.Other challenges highlighted by the panel included:Siloed departments that limit communications and visibility within organizationsA dearth of subject matter expertiseThe possibility of simultaneous AI requests from multiple regulatory agenciesA more remote and dispersed workforce due to the pandemicUse Cases for AI and ComplianceIn order to meet these challenges head on, companies are increasingly turning to AI to help them comply with new regulations. Some companies are partnering with technology specialists to meet their AI needs, while some are building their own systems.Anheuser-Busch is one such company that is using an AI system to meet compliance standards. As Kimberly Pack, associate general counsel of compliance for Anheuser-Busch, described it: “One of the things that we’re super proud of is our proprietary AI data analyst system BrewRight. We use that data for Foreign Corrupt Practices Act compliance. We use it for investigations management. We use it for alcohol beverage law compliance.”She also pointed out that the BrewRight AI system is useful for discovering internal malfeasance as well. “Just general employee credit card abuse…We can even identify those kinds of things,” Pack said. “We’re actively looking for outlier behavior, strange patterns or new activity. As companies, we have this data, and so the question is how are we using it, and artificial intelligence is a great way for us to start being able to identify and mitigate some risks that we have.”Artificial intelligence can also play a key role in reducing the burden from alerts related to potential compliance issues or other kinds of wrongdoing. The trick, according to Alex Lakatos, partner at Mayer Brown, is tuning the system to the right level of sensitivity—and then letting it learn from there. “If you set it to be too sensitive, you’re going to be drowned in alerts and you can’t make sense of them,” Lakatos said. “You set it too far in the other direction, you only get the instances of the really, really bad conduct. But AI, because it is a learning tool, can become smarter about which alerts get triggered.”Lakatos also pointed out that when it comes to the kind of explanations for illegal behaviors that regulators usually want to see, AI is not capable of providing those answers. “AI doesn’t work on a theory,” he said. “AI just works on correlation.” That’s where having some smart people working in tandem with your AI comes in handy. “Regulators get more comfortable with a little bit of theory behind it.”H5 has identified at least a dozen areas related to compliance where AI can be of assistance, including: key document retention and categorization, personal identifiable information (PII) location and remediation, first-line level reviews of alerts, and policy applicability and risk identification.Data Classification, Methods, and ApproachesThere are various methods and approaches to data classification, including machine learning, linguistic modeling, sentiment analysis, name normalization, and personal data detection. Choosing the right one depends on what companies want their AI to do.“That’s why it’s really important to have a holistic program management style approach to this,” said Eric Pender, engagement manager at H5. “Because there are so many different ways that you can approach a lot of these problems.”Supervised machine learning models, for instance, ingest data that’s already been categorized, which makes them great at making predictions and predictive models. Unsupervised machine learning models, on the other hand, which take in unlabeled, uncategorized information, are really good at data pattern and structure recognition.“Ultimately, I think this comes down to the question of what action you want to take on your data,” Pender said. “And what version of modeling is going to be best suited to getting you there.”Setting Expectations for AI DeploymentOnce you’ve determined the type of data classification that best suits your needs, it’s crucial to set expectations for the AI deployment within your company. This process includes third-party evaluation, procurement, testing, and data processing agreements. Buying an off-the shelf solution is a possibility, though some organizations—especially large ones—may have the resources to build their own. It’s also possible to create a solution that features elements of both. In either case, obtaining C-suite buy-in is a critical step that should not be overlooked. And to maintain trust, it’s important to properly notify workers throughout the organization and remain transparent throughout the process.Allowing enough time for proper proof of concept evaluation is also key. When it comes to creating a timeline for deploying AI within an organization, “it’s really important for folks to be patient,” according to Pender. “People who are new to AI sometimes have this perception that they’re going to buy AI and they’re going to plug it in and it just works. But you really have to take time to train the models, especially if you’re talking about structured algorithms and you need to input classified data.”Education, documentation, and training are also key aspects of setting expectations for AI deployment. Bear in mind, at its heart implementing an AI system is a form of change management.“Think about your organization and the culture, and how well your employees or impacted team members receive change,” said Timia Moore of Wells Fargo. “Sometimes—if you are developing that change internally, if they’re at the table, if they have a voice, if they feel they’re a meaningful part of it—it’s a lot easier than if you just have some cowboy vendor come in and say, ‘We have the answer to your problems. Here it is, just do what we say.’”Keeping AI Solutions Compliant and Avoiding BiasWhen deploying an AI system, the last area of consideration discussed by the panel was how to keep the AI solution itself compliant and free of bias. Best practices include ongoing monitoring of the system, A/B testing, and mitigating attacks on the AI model.It’s also important to always keep in mind that AI systems are inherently dependent on their own training data. In other words, these systems are only as good as their inputs, and it’s crucial to make sure biases aren’t baked into the AI from the beginning. And once the system is up and running—and learning—it’s important to check in on it regularly.“There’s an old computer saying, ‘Garbage in, garbage out,’ said Lakatos. “The thing with AI is people have so much faith in it that it is become more of ‘garbage in, gospel out.’ If the AI says it, it must be true…and that’s something to be cautious of.”In today’s digital world, AI systems are becoming more and more integral to compliance and a host of other business functions. Educating yourself and making sure your company has a plan for the future are essential steps to take right away.The entire H5 webcast, “New Rules, New Tools: AI and Compliance,” can be viewed here.ai-and-analytics; data-privacyccpa, gdpr, blog, ai, big-data, -data-classification, fcpa, artificial-intelligence, compliance, ai-and-analytics, data-privacyccpa; gdpr; blog; ai; big-data; data-classification; fcpa; artificial-intelligence; compliancemitch montoya

The 2021 CLOC conference focused a lot on applying product principles to legal services. General Counsel are often in the position of having to show the value of their team’s services and why, as a cost center, it makes sense to continue to grow their department or to buy technology to support their department. In addition to showing that value, there is pressure to be more efficient while providing excellent customer services. By productizing services, you can provide repeatable, measurable solutions that address the needs above. There is also the great benefit of being connected to your client’s needs by providing the services that match the most pervasive and urgent needs. However, if you don’t have a background in product management, how does one go about productizing legal services, and what does that even mean? As someone who is Pragmatic Marketing Certified through the Pragmatic Institute, I am here to help. This blog, and the blog series to follow, will show you how to get started, interview people internally to understand the needs, position your existing solutions internally, and make build vs. buy vs. outsourcing decisions. Let’s start with a high-level overview of where to begin.What does productizing legal services mean? Productizing your legal services focuses on creating solutions that apply to multiple customers in a repeatable way. This means that you first have to understand your customers’ problems by listening, asking, and observing. It then means that you create several repeatable processes to address those problems. Finally, it means you market those solutions internally and show how they bring value to the business. Taking it one step further, it also means that you leverage technology to support these services and continue to develop and improve the services based on feedback.So how does one go about creating these solutions inside a legal team? The first step is all about understanding the needs of the business. You can look internally at the requests the legal department receives to get an understanding of what the business is coming to the legal department for. Next, you want to speak to leaders from different groups in the business to understand what legal needs exist that are not coming into the legal department but should be addressed. Which leaders to speak to will depend a bit on your organization but I would recommend connecting with the following, at minimum: sales, finance, engineering (or product) as well as regional leaders in any key regions. More on this to come in my next blog on interviewing people internally to understand the organization’s needs.Once you have the information, it is helpful to create a list. I like to use the format below:Problems to SolveOnce you have a pretty solid list, you should brainstorm high-level recommended solutions (not the detailed how). This will include things like solving a certain need through documentation (e.g. a “how-to guide” or a template contract). It may include things like facilitating the intake of legal requests or facilitating access to contract information. Once you have your list of potential solutions, there are two next steps. For the set of existing solutions, you should group those into categories and make sure that you are adequately marketing and reporting on those (more on this in a future post). For the set of solutions that are future state, identify how you are going to address this need. When looking at the gaps, I like to categorize the gaps in the following ways so I can understand the budget impact and the division of work.Note that urgency speaks to how quickly the need needs to be solved overall and not necessarily the urgency of a specific request. For example, it speaks to how urgently people need a contract database as opposed to how quickly someone needs information about a specific contract. Pervasiveness addresses how many internal departments/employees have this need. Is it centered around just a small group within one department or is it a need expressed by multiple departments? The relationship to the company strategy should be focused on how much this need moves the business forward. Does it facilitate the company’s #1 strategy? When you complete this list, I recommend grouping it into like needs. If there are overlapping needs, you may want to create a consolidated item but make sure you capture the pervasiveness of it.Recommendations for Filling The GapsBy going through the above process you will have a good understanding of the various needs and solutions in your organization. In the next blog in the series, I will overview how to interview people internally to understand the organization’s needs.legal-operationslegal-ops, blog, legal-operations,legal-ops; bloglighthouse

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

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