The legal landscape is developing rapidly, and one of the essential disruptors in today’s law firms is the adoption of predictive analytics to examine the enormous volumes of data that lawyers, particularly litigators, must sort through. Two main fields within litigation are particularly affected by predictive analytics: case law research and e-discovery. Artificial intelligence (AI) programs are ushering in the kind of technology that will change the way research and discovery methods are followed, and many small firms are clasping their futures on the notion that the development of AI and its applicability in the legal setting will remain to catch on with attorneys and firms, delivering clients their way. Client expectations have changed, and there are constant demands to be more productive and produce more pocket-friendly services. Whether a prosecutor, legal librarian, or paralegal, their work includes finding and incorporating relevant information more immediately, even as the body of data they are sorting through continues to increase. This is an issue that must be addressed because the requirement from clients is not lifting.
Fortunately, there are already practical solutions in the industry today that target many—though admittedly not all—of these data-related hurdles. Technology-focused firms have been engaged in the legal research area for nearly half a century now, and they have acquired a lot about how to handle big sets of data. These providers are also funding heavily in the hope of language-based technologies that can investigate, interpret, and amalgamate the written report. These solutions are approving the “data-driven lawyer,” for whom the critical data phenomenon represents an opening, rather than an unmanageable problem.
Technology businesses in the legal research space add hoards of documents to their receptacles every day. Such numbers require purpose-built platforms that can process massive data volumes at high velocity. Additionally, these vast data volumes must be enhanced so that additional insights can be excerpted. With the help of venerable data sorting technology and document meta-data enrichment, raw data is refined, improved, filtered, and structured so it is more easily searchable and can be dug for specific insights that are undeviatingly relevant to today’s legal questions.
A uniformly promising technology whose potential can be neglected amid all the AI hype is data analytics. Data analytics is contributing real benefit and value to legal practitioners right now. Constitutional analytics power responsible decision-making in some legal discipline areas such as copyright and trademark law, copyright, protection, antitrust, and business litigation.
Data analytics endows new legal insights by mining extensive data sets: legislation, case opinions, docket data, and client contracts to name a few. By processing this enriched data, lawyers can conclude opposing counsel, judges, litigation parties, and contract drafts to communicate insights that were not beforehand knowable.
Legal analytics is also being practiced to help firms improve the ways they propose the business of law. It does so by offering factual data from millions of trial records about the behavior and presentation of law firms and personal lawyers—including data points like win rates, cases with resolutions, time to the injunction, in particular areas of law. Analytics can also be used to pursue broad industry trends associated with activities like strategic planning, business improvement, and marketing.
