It is no secret that legal analytics is changing the everyday practices of litigators. In recent
years, dozens of commentators have published their own predictions on the ways in which
technology will transform, disrupt, or revolutionize the legal industry. Even the American Bar
Association has followed suit, modifying a lawyer’s duty of competence to include knowledge of
substantive law as well as “the benefits and risks associated with relevant technology.”
Legal analytics is not new. For hundreds of years, litigators have capitalized on the advantages of evidence-based decision making. This is, in fact, largely how the law works. Litigators serve their clients by using their legal reasoning skills to apply specific facts to particular laws. They do this by collecting evidence and by identifying the relevant statutes, cases, or rules that may support their case. For many years, this was a tedious and laborious process. However, recent innovations in legal technologies have expanded the depth and the breadth of legal research, allowing litigators to quickly mine, parse, synthesize, and utilize large bodies of public and private records.
Barriers to Legal Analytics
The widespread adoption of legal technology has been slow, a growth that has been stymied by functional and practical barriers. The technical tools available for legal analytics are still in their infancy. This state of affairs is compounded by the fact that large, comprehensive bodies of court data still remain out of reach in many jurisdictions.
As innovators, legal tech companies confront a number of challenges. One of these challenges involves the curation of a clean, usable database. Legal analytics can produce inaccurate or misleading information because of how court data is structured. For example, collecting historical information about individual attorneys and law firms can be problematic. Attorneys move from one firm to another. Law firms add and delete named partners. Often, though, legal documents simply contain typos or misspellings, creating tensions between sense and reference that can render it difficult to locate and track legal entities across a given dataset.
Or, consider the court documents and docket sheets. Many of these files lack standardized terminology, the kind of standardization that enables quick-and-easy identification of motions, issues, and rulings. This means that a user may have to generate an exhaustive list of possible expressions before mining the court documents. This task is complicated by the fact that judges and parties may use a variety of linguistic strategies to identify, describe, or present statutes, claims, and outcomes. Not only that, but a particular document may contain a number of separate claims, which may require a clever search algorithm that can disaggregate each outcome from each claim. Creating such a set of search functions can require a substantial front-end investment.
There are also practical barriers to the adoption of technologically-driven legal analytics. Many have highlighted the legal profession’s aversion to change, suggesting that there is something about the culture of lawyers that ties them to traditional legal practices. However, a more useful insight concerns the practical integration of legal analytics into the business models of law firms. Historically, lawyers have passed along the costs associated with legal research to clients. If a firm purchases flat-rate access to a legal analytics platform, the conversations surrounding the distribution of research costs must change. No longer is it simply a conversation between a lawyer and their client about the expenses of a case. It is now a conversation between lawyers and vendors about overhead.
Knocking Down the Barriers
Until recently, litigators gathered insights into the behaviors of individuals, parties, and cases by relying on the anecdotes of colleagues who had personally dealt with similar matters. Anecdotal data can be informative. The problem, however, is that it relies on a small sample size. This means that it can easily provide an incomplete picture of reality, leaving its users with a series of questions about the accuracy of that depiction.
By harnessing the power of big data, technologically informed legal analytics can generate new insights into how the legal process works. With a large enough dataset, users can identify the trends and the tendencies that hide behind the exceptions and the outliers, allowing litigators to implement smarter legal strategies and craft stronger legal arguments.
Technology-assisted review (TAR) was one of the first major applications of artificial intelligence in legal practice. TAR allows attorneys to organize, analyze, and search very large, very diverse data sets for e-discovery projects. The efficiencies of TAR are undisputable, with trained machines reviewing documents much more quickly and much more accurately than human counterparts. Other technical tools enable litigators to study hundreds of examples of successful pleadings. By curating searchable archives of tentative rulings, litigators can use these third-party platforms to collect judge-specific, issue-specific, or venue-specific templates for different types of motions. Litigators no longer need to start from scratch. They can rely on legal arguments and legal strategies that have already been tried and tested.
Litigators now have much more to offer their clients than anecdotes and hunches. What are the predilections of your judge? Should you request a change of venue? Has your opposing counsel litigated a similar case? When should you consider settling your case? Legal analytics enables litigators to answer these questions with data-driven insights, insights that can help clients make informed decisions about their case.
The Future of Legal Analytics
Legal analytics is here. The question remains, how will legal analytics be used? Will it be used by litigators to perform predictive analytics, to guide the development of litigation strategies? Will it be used by support staff to help law firms identify and capitalize on broad litigation trends? Or will it be used by marketing teams to solicit new clients, to craft pitches that compare the firm’s track record with those of competing law firms? The uses are endless.
We do know that the broad applicability of legal analytics has the potential to level the playing field between large and small firms. It has the potential to radically reconfigure the relationships between clients and attorneys, helping to open the black box of civil litigation in ways that allow both to become more informed participants in the decisions about any given case.
About the Author
Nicole Clark is CEO and co-founder of Trellis Research Business litigation and labor and employment attorney.
Trellis is an AI-powered legal research and analytics platform that gives state court litigators a competitive advantage by making trial court rulings searchable, and providing insights into the patterns and tendencies of your opposing counsel, and your state court judges.