Big Law is finally beginning to understand the tremendous value of Big Data. In the legal industry, raw data abounds, but it is only useful to litigation departments when it is organized, made comparative and capable of informing behavior. In other words, data is of the greatest value when it is actionable.
For many years, litigators have struggled to make sense of courthouse data, but now that the technology exists to simplify and visualize this data in a way that conveys meaningful patterns and trends, litigators are learning to base their actions on comparative data analysis. And there is nothing a litigator likes more than a reduction in legal guesswork.
Legal analytics is a class of artificial intelligence (AI)-powered software solutions that analyze legal Big Data to provide key insights and reduce this guesswork. Legal data without actionable insights is just information. Insights are generated by comparatively analyzing data, identifying statistically significant information and developing appropriate conclusions.
Judicial analytics, a subset of legal analytics, offers previously unavailable insights into judicial behavior. When data suggests, for example, that a specific judge rules in favor of defendants in employment cases twice as often as the county average, that is actionable information that leaps off the page and helps litigators customize their litigation strategies. In this context, “actionable” means any measurable litigation data that can serve as the basis for a strategic or tactical litigation decision. For instance, a plaintiffs’ attorney might make a tactical decision to file a CCP § 170.6 peremptory challenge against a judge if data suggests that the judge is significantly more likely than the county average to rule for defendants on summary judgment motions — or, for that matter, if the judge receives CCP § 170.6 filings by plaintiffs at a higher rate than her peers. This is just one example of judicial data made actionable.
Lawyers can sometimes be resistant to new, technology-enabled tools for preparing case strategy and tactics, which is precisely why it is incumbent upon the legal tech community to articulate the “actionable” value of judicial data trends. Litigators don’t particularly care about the nuances of AI and machine learning — they just want to improve their chances of successfully representing their clients’ interests. A prepared litigator is a happy litigator. How better to prepare for a case than to know, beyond a doubt, your judge’s anticipated rulings and behavior?