The business and practice of law requires attorneys to interact with and make sense of significant amounts of data. Data analytics, or “big data,” plays a pivotal role in enabling lawyers to work with and transform this information into useful, practical, strategic knowledge. From developing a case strategy to refining business pitches for prospective clients, data analytics is evolving into an integral part of a successful legal service and business model, and is considered the “wave of the future,” according to Mark A. Lemley, Stanford Law School professor.
“The entire process . . . from determining whether to take on a case and analysing any of the (court) documents related to it through to researching, contracting, and even billing, can be significantly improved with big data analytics,” says Mark van Rijmenam, founder of Datafloq. According to Van Rijmenam, a lawyer’s ability to harness the significant amounts of data available today using predictive, prescriptive, or descriptive analytics will help him or her remain competitive in today’s data-driven landscape. Also known as legal analytics, for the litigator, this process involves mining data contained in case documents and court dockets, and then aggregating that data to provide previously unknowable insight into the behavior of individuals, organizations, and the subjects of lawsuits, according to Owen Byrd, chief evangelist and general counsel of Lex Machina.
A Bit about Analytics
Analytics is the engine that powers data-driven legal practice, relying on advanced tools that can “take massive volumes of legal data, structure it, and strip out irrelevant or redundant information and make it readily searchable and comprehensive to users looking for very specific kinds of information,” says Jeff Pfeifer, vice president of product management for LexisNexis. According to Pfeifer, “these are tasks that would take humans weeks, months, or more to effectively complete.” Today, there are various types of analytics, and they are explained briefly below.
- Descriptive analytics (DSA). DSA looks back in time to tell you what happened, extracting and organizing data to identify legal trends and assess the behavior of those involved in litigation. It also highlights additional factual information for use to assess potential outcomes of cases, cultivate strategies, and estimate case values and litigation costs. Advanced data visualization is a critical instrument in DSA. Visualization helps users spot trends, patterns, and insights behind the data that would otherwise be hard to find with text review alone. Visualization is also used, turning complex data into easy to digest charts and graphs. Collectively, DSA increases the accuracy and efficiency with which relevant data are comprehended so that key decisions can be reliably based in fact.
- Predictive analytics (PA). PA, by contrast to DSA, turns its attention to the future. PA determines the probability of a future event or the probability that something new is similar to something already seen in the data. It uses various statistical methods—i.e., predictive modeling, machine learning, and regression analytics—to make reasoned predictions about what may happen in the future.
- Prescriptive analytics (PSA). PSA recommends specific courses of action and informs on likely outcomes from specific actions. This emerging technology provides advice and refines its recommendations by tracking outcomes of actual decisions and incorporating that information in future recommendations.
Who’s Using Data Analytics?
Data analytics is here to stay: “99% of respondents indicated that analytics will be ‘indispensable’ to the practice of law,” and “77% of legal departments will spend more on eDiscovery analytics this year” than they did last year, according to the Data Analytics in the Legal Community: 2016–2017 Trends survey conducted by the Coalition of Technology Resources for Lawyers (CTRL). CTRL further identified top uses of data analytics by in-house legal departments and found that the top three were e-discovery, legal matter management, and information governance. With respect to e-discovery, legal departments’ top three uses for data analytics in this area were culling, early case assessment, and relevancy review. These were followed by review prioritization, sampling/quality control, and fact finding.
“The step-by-step e-discovery process to which we’ve become accustomed with linear review is no longer going to be the rule of e-discovery workflow,” says Dave Copps, founder and CEO of Brainspace, a leader in data analysis. According to Copps, “[o]ne new approach is to begin with the ingestion of data and then to rely on powerful software tools, such as advanced visual analytics, to provide earlier insight into the case.” Additional new functions range from graphical views of data and more transparent reporting metrics to new sophisticated search capabilities and advanced data mining tools.
In addition to e-discovery, lawyers are also using big data in the following ways:
- Case cost assessment. Firms are using data analytics to assess the potential profitability of a case or case type. Firms are also using big data services to become more informed on what they should charge. Some compare a matter’s costs against average industry costs in order to demonstrate efficiency to their clients despite the appearance of their rates being higher.
- Intellectual property (IP) and IP litigation. Data analytics can be used to efficiently facilitate demonstrating similarities in a creative work. Likewise, analytics and software forensics are being used to quickly determine whether two pieces of software are similar in design. The IP arena is ideally suited for big data tools and techniques because of its high volume, high variety, and high velocity of changes. And IP departments can apply larger data sets and smarter analytics to drive value in the creation and protection of IP.
- Human resources counseling. Analytic tools can be used to counsel clients on hiring practices, attrition projections, head count needs, compensation administration, and liability exposure. These are just a few aspects of employers’ daily challenges that lawyers can assist their clients with using data science strategies. Legal analytics allows attorneys to help clients make better, faster, more cost-effective workplace-related decisions.
“Lawyers and companies make decisions today based on ‘anecdata,’” says Lemley. Data analytics presents an opportunity to litigators to distinguish themselves and be able to provide better service to their clients, says Scott Reents, member of The Sedona Conference Working Group on Electronic Document Retention and Production (WG1) and the E-Discovery Counsel Roundtable. “A lot of what you read about . . . data analytics is about automation: replacing lawyers, being able to do things automatically. I think that’s a very small part of the story. The bigger story is that there is all this information out there, and we’re drowning in it. . . . Being able to have better information faster is a critical advantage in litigation. It helps you to be more nimble,” according to Reents.
Legal analytics is a mechanism to understand and interpret data in a quick and efficient manner. Finding the right combination of analytic tools for use in the practice of law is important because it is not a one-size-fits-all approach. Taking advantage of these platforms will not only allow for more efficient delivery of legal services to clients but will also provide for a competitive advantage in one’s practice.
Daniel S. Wittenberg is an associate editor for Litigation News.
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