We are in the midst of what some have deemed “the golden age” of legal technology startups. Until relatively recently, however, there was a lack of innovation in terms of how attorneys utilized Big Data. Early efforts to tackle Big Data and adapt artificial intelligence (a.k.a AI) and machine learning concepts for the legal industry were concentrated on topics such as billing and document management. In the past few years, the legal tech sector has also begun exploring data-driven tools for substantive legal research. Judicial analytics is at the crest of this wave, and is completely revolutionizing the legal profession.
The massive United States court system is the epitome of Big Data. In this sea of data lives valuable, actionable litigation insights just begging to be uncovered. However, finding even basic — let alone actionable — information about judicial behavior has always been a challenge, largely because of the sheer scope and size of data involved.
For example, the California Superior Court system is the largest in the country and serves over 39 million people, or 12 percent of the U.S. population. The system handles nearly 7 million case filings each year, has nearly 2,000 judges spread over 58 different counties and accounts for a substantial portion of the total U.S. state-level litigation volume each year. Los Angeles County alone has nearly 600 Superior Court judges, each of whom handle over 3,300 cases per year.
Owing to the complexity and difficulty of acquiring and handling litigation data, particularly at the state trial court level, most litigators have relied heavily on office-wide emails, anecdotes and even their “gut” when anticipating a given judge’s behavior, to the extent they even try to predict judicial behavior at all. Thanks to the burgeoning field of judicial analytics, this guesswork is a thing of the past. We are now able to analyze vast amounts of scattered, expensive and “messy” state litigation data to help attorneys customize their litigation strategies to their particular judge, win significant motions and, most important, offer informed, data-supported counsel to their clients.
Applied judicial analytics enables litigators to obtain “scouting reports” on a specific judge, and to effectively tailor their business pitch, as well as discovery, motion and trial practice to that judge. If I told you that a certain judge granted motions to compel discovery filed by plaintiffs at twice the rate of his peers, wouldn’t that help inform your approach to discovery and motion practice? And if I told you that one judge was 75 percent more likely than the county average to deny summary judgment motions filed by employers in discrimination cases, wouldn’t that be something you’d factor into your litigation or settlement strategy? Therein lies the true value of judicial analytics — the ability to access the unique “jurisprudence DNA” of each judge you are called to appear before, and to calibrate your litigation strategy accordingly.
With the exception of the digitization of caselaw, there is perhaps no more significant a development in the realm of legal research than the rise of judicial analytics. The genie is out of the bottle. As attorneys become more accustomed to using judicial analytics to guide and inform litigation strategy, it will become standard practice at the commencement of, and throughout, every case. One day, the notion of attempting to “guess” how a judge might rule on a motion, and failing to customize your litigation strategy to the judge, will be considered absurd, and perhaps even support a claim for malpractice. That day is not very far away.