In 2013, Barrett & Farahany commissioned a study to determine the outcomes of cases at a motion for summary judgment. The results were shocking: over eighty percent of cases were dismissed at summary judgment by the federal district court in Atlanta. A further study revealed that Atlanta’s federal court dismissed more cases at summary judgment than almost any other jurisdiction.
After the study was published, scholars, attorneys, and judges opined on what the study results meant. Many decided that the high dismissal rates were because of bad lawyering, bad case selection, and/or bad law. The prevailing belief was that good cases settled. The data from the study was able to definitively show what happened (i.e., how many cases were dismissed and by whom), but what the data could not reveal was the “why” behind the high rate of dismissals.
At the time, businesses were utilizing analytics, data mining, artificial intelligence and other methods to determine business needs, trends, and solutions, yet the legal world had not yet adopted the same approach. This year, Georgia State Law School started its Legal Analytics Lab in conjunction with its Business Analytics lab, which was already performing these kinds of big data analytics for businesses. Law Professor Charlotte Alexander leads the Legal Analytics Lab, and she began discussions with Barrett & Farahany managing partner, Amanda A. Farahany, to explore how employment discrimination cases are handled.
Starting in September 2017, Barrett & Farahany and Georgia State University began gathering documents for the summary judgment project, including downloads of all dockets, complaints, and summary judgment orders for the Georgia federal courts from 2010 through the end of 2017. This data, along with information from several other data sources, will comprise the ‘big data’ that will now be mined to determine the missing “why”: why employment discrimination cases are being dismissed at summary judgment so frequently. In addition, the outcome of the study will provide predictive analytics that will help determine which factors lead to good client outcomes and which do not. Finally, the project will seek to identify the ‘playbooks’ for successful plaintiff lawyers, for employment defense lawyers – and for the judges.
It is a new and exciting era in technology. Can data analytics provide this type of information? We shall shortly see.
