This is What Actionable Data Looks Like

March 20, 2018,

Did you know that there is one Los Angeles County complex civil litigation judge who receives significantly more CCP §170.6 peremptory challenges from plaintiffs than his peers? It might not surprise you that he’s also, objectively, one of the most defendant-friendly judges in the county.

This data was sourced from Gavelytics’ AI-powered judicial analytics platform and is just one example of how statistical data regarding a judge’s past behavior can drive litigation strategy. The days of having to guess or crowdsource whether to “ding” your assigned judge are over. Litigators now have the technological tools to make informed decisions based on judicial tendencies. This is what actionable data looks like.

Let me offer you another example, also from Gavelytics’ database. All but one L.A. County complex civil litigation judge materially leans more towards defendants in bench trials. This is the kind of information you want to know when heading toward trial, right? While judicial analytics technology is no crystal ball, it affords litigators the opportunity to stay ahead of the game, and provide insightful, fact-based counsel to their clients.

I know this might come as a shock, but judges are, in fact, just people. And, like all people, they have their own set of preferences, tendencies and biases. Some are difficult to persuade. Others are more malleable. Some move quickly through their cases. Others are more deliberate. Some like to look for an opportunity to dispose of a case on summary judgment — while others hesitate to wipe out an action at this early stage. This has long been the reality of litigation. But now that we can access and decode judicial analytics, judges’ propensities are no longer shrouded in mystery. This is what actionable data looks like.

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