Legal analytics—the use of data to make quantitative legal predictions that inform decisions—is currently moving from the margins of the profession to the mainstream. Underlying the rise of analytics is the maturation of artificial intelligence technologies like natural language processing and machine learning, which are deployed to add structure to complex legal data, which in turn can be used for statistical analysis.

At its core, most legal data is relatively “unstructured,” lacking tags or metadata that help computers understand meaning in documents. Now, with advances in machine learning, attorneys, editors and other subject matter experts can help train computers to add the missing detail to structure vast amounts of legal data, enabling machines to replicate human editorial activities at scale. With clean, structured data, companies can then create powerful new tools that identify important legal trends and help attorneys make better legal and business decisions.

Previously, this series examined why comprehensiveness and quality matter when using legal analytics, and how analytics can sense patterns in the way judges rule. Now, we're taking a look with some specific ways analytics have evolved and what it all means for your practice.

Expert Witness Analytics

Legal analytics has now extended beyond analysis of the behavior of judges, attorneys and parties to litigation. Expert witnesses often provide critical support for attorneys' arguments, submitting testimony related to complex scientific areas like medicine or patents. When a legal team is looking for an expert with a good track record of having testimony admitted in court, or needs to prepare to challenge opposing counsel's expert witness, legal analytics can provide significant help by providing summaries and dashboards that quickly show how a given expert has fared in court and why.

It is not atypical for an attorney to reach out to other attorneys they know or send a firm-wide email and ask if someone can recommend experts in a specific field. The recommendations that come back are often based on sparse data or anecdotes, and the information is further muddied by the personal relationships that attorneys develop with certain experts. Now, analytical tools can show you more than just biographical and professional information about expert witnesses. Not only can you learn whether they appear more frequently for plaintiff or defense, or how often have they provided testimony in state versus federal courts, but you can also get detailed metrics on how successful the expert has been in getting testimony admitted in court.

Using the Daubert standard for expert witness testimony, you can use legal analytics to create scorecards to show every time an expert's testimony was challenged in court and why. Was it for poor methodology, lack of relevance or lack of qualifications? This type of granular analysis can only come from mining a judge's writings, where the rulings and the reasons for those rulings are articulated. Now, with analytics, when an attorney learns the name of experts opposing counsel is using in court, the attorney can quickly look them up, find previous cases when they have been successfully challenged and use the same reasoning to have an expert's testimony excluded again. This is valuable data that can influence both judge and jury.

Compare Your Firm to the Competition and Understand Your Own Firm Better

Firms are also using legal analytics to learn more about themselves and the competition. While comparisons based on overall revenue and profits per partner have long been available, with data extracted from dockets and case law, firms can now compare substantive results too. Which firms are winning the most motions to dismiss in a particular area of law, in front of certain judges? This type of data can be key to winning business. A partner that can show that their firm has better outcomes in certain courts for certain issue types will have a compelling case to make for a client facing litigation focused on that issue. When a firm is looking to expand business with a client, it can use analytics to see which firms the client uses for other legal matters, then create stronger pitches that directly and effectively differentiate the firm from competitors.

Of course, the same tools can be used by clients. Corporate users of legal analytics are getting better information about firms by using legal analytics to evaluate their outside counsel. Corporate counsel can instantly determine who has more experience or better win rates in front of certain judges or in certain subject areas. This means they can hire more effectively and they can use the insights they've gleaned to have a stronger hand in negotiations with firms. Historically, this type of data has been almost impossible to come by, as it would require companies, oftentimes competitors, to share internal data about their litigation experiences. Using analytics gleaned from public records, they now can better choose their counsel based on real results.

The same tools can also help firms plug gaps where they are otherwise lacking expertise. With analytics focused on their own litigation practice, law firms can identify gaps in their own team and hire to fill those gaps. Or, if a firm is litigating in a state where it otherwise does not have a particular practice, analytics can help determine which other firms to work with, or even the individual attorneys that are most successful in certain venues. One firm noted they saw a trend in which a certain judge favors graduates of local law schools; accordingly, they try to send only attorneys from those schools to argue in front of that judge.

Conclusion

As adoption rates for legal analytics tools rise, we are seeing the data-driven lawyer of tomorrow is increasingly a reality today. Whether legal professionals want to be more persuasive in court, do a better job identifying a reliable expert witness, or simply know more about their own firm and attorneys, they are discovering that legal analytics exposes an array of data points and insights that otherwise have been the province of anecdotes and speculation. As technology continues to improve, our ability to extract and classify legal language will improve and the types of questions legal analytics can answer will continue to multiply. Questions that once seemed unanswerable are now answered with a quick dashboard lookup. With strong adoption at most of the largest law firms already, the legal market is poised to continue to incorporate more quantitative tools into both the business and practice of law.

Nik Reed is the co-founder and COO of Ravel Law, a legal research and analytics platform acquired by LexisNexis in 2017. While at Stanford, he co-founded Ravel, as a joint research project between the law school, the design school, and the computer science department, with the goal of using modern AI based tools to help attorneys better perform legal research. Prior to Ravel, Nik worked in management consulting where he focused on international mergers and acquisitions in the technology industry.