Who is a Legal Data Scientist?
At its core, a legal data scientist is a lawyer who is also an expert in data organization and analysis. In other words, they are lawyers with a high level of data literacy. They usually have a firm understanding of the technical components of the issues facing their company or organization, and can use this understanding to convey how the information can be transformed into data. Once the data is organized and structured, they can build predictive models based on this data to deliver business critical information to decision makers .
A legal data scientist likely understands how and when to use a data visualization tool to present important information to decision makers such as compliance officers, General Counsel and others at the C-level. Such tools enable the legal data scientist to visualize legal data so that decision makers can more readily consume the information. A legal data scientist can bridge the gap between compliance, analytics and the C-Suite, demonstrating how legal data impacts key business decisions.
Necessary Skills for Legal Data Scientists
When it comes down to the nitty-gritty, what does it take to be a legal data scientist? First and foremost, a legal data scientist must possess a strong legal background. Whether that be in-house or private practice experience, working with lawyers, courts, techno-law, and the like on a daily basis is an absolute must. But of equal importance is the ability to speak and understand data and analytics. From statistical analysis to big data to algorithmic models to basic knowledge of database systems, a strong analytics background is equally as important for a legal data scientist as a legal proficiency. More often than not, a legal data scientist emerges from the IT side of the house rather than the legal side.
Data Science Applications in the Legal Field
The applications of quantitative analysis in legal practice are numerous. In fact, it is difficult not to find a possible application for a legal statistician in the modern firm.
Legal data scientists have revolutionized case analysis. Advanced analytics are changing how attorneys approach cases. Electronic discovery companies like Recommind, Ipro, Clearwell, and Logikcull are using advanced statistical analysis to target cases and provide more relevant results. Programs like Casetext and Ravel law provide advanced searching and other features that assist with case and statute analysis. Northpointe uses an extensive array of factors including legal defense statistics to predict recidivism. Programs like LexPredict allow you to predict outcomes of court cases. We can now search for cases based on party pleadings, judge history, expert witness feedback and dozens of other criteria.
Data analytics also provides innovative ways to approach litigation strategy. Collaboration with other firms will eventually allow firms to share information on opposing parties and judges. Eventually this will mean each firm will know every party’s strengths and weakness. Advanced analytics will soon be able to predict outcomes in lesser-known author’s opinions. Equally as important is the growing amount of public data available to researchers. Things like average criminal penalties, or jury preferences are becoming more easily available. Legal data science can add a great deal of predictive value to this type of data.
Legal data scientists are already reshaping the market for expert witnesses. The American Journal for Testing and Measures released in 2002 a study that proved paying an expert witness a premium for testimony will bias their opinions. If you give an expert witness $800/hour and a competitors $75/hour you are going to get a $700/hr opinion. Due to this bias, economists already analyze cases to determine reasonable compensation for expert testimony. Instead of paying an expert witness for side experiments, a firm can pay a data scientist to work with millions of similar cases and find a mean payment rate for relevant contracts to the case at hand. This way the firm is not biased by dollars in the same way juries are not biased by dollars.
Influence of Legal Data Scientists on the Law Sector
The role of legal data scientists has a profound impact on the manner in which legal services are delivered. In the past, the analysis of litigation data typically fell on the shoulders of senior experienced attorneys with a little to no technology or data science training. They would find themselves looking through large amounts of data and attempting to draw conclusions ad hoc based on what they observe in the data.
The onset of legal data scientists has automated many of these processes such that skilled attorneys or junior associates can analyze the data quickly. In addition, by using sophisticated machine learning algorithms, those conclusions can be aggregated in a "mining" type of activity or "data reduction" activity. This allows for improved accuracy in the quantitative measurements and predictive analytics resulting from the science.
More efficient delivery of the services means greater profitability for law firms. It also means clients can receive more and better information in real time which allows for real time decisions. Machines can make decisions in milliseconds today. Humans cannot. Being able to present information instantaneously , with the quality assurances of the machine learning algorithm underlying the interface, ultimately helps clients improve their resolution of the matter.
Big data is transitory. It is the collection and aggregation of data. Once integrated into usable information, it is not the "big data" that people rely upon for decision making. It is the predictive analytics as a result of the data analysis. The speed of data science ensures that the activity is available at or near the time(s) it is needed.
When lawyers need to rely on increasingly large data sets for their legal practice, having good data and data science interfacing with the user will ultimately determine who wins the war for clients and, subsequently, their lawyers.
Obstacles for Legal Data Scientists
As is the case with any rapidly-evolving field, legal data science is not without its challenges. Data privacy is a particular sticking point; lawyers are bound by strict data protection laws, which CADS must abide by when they collect and process data. The lawyers that CADS lawyers support are likewise charged with maintaining confidentiality in their dealings with clients, all the while facing a public increasingly uncomfortable with how its data is being used.
Maintaining data accuracy is another area where CADS must tread carefully. Computers are only as reliable as the information with which they’re programmed and this is especially true for CADS. A single error, or outdated dataset, can cause enormous problems down the line. This is more often than not the crux of CADS work, as even the most advanced machine learning program is only as useful as the data it’s based on. Although this isn’t solely a legal issue, it’s nevertheless one with which CADS must contend.
As legal practice is a conservative industry, integrating into legal departments is a constant work in progress. Lawyers are, as a rule, chary of change. This can make it difficult to have CADS seen as an asset rather than a disruptor to the status quo.
Future Prospects for Legal Data Science
Future trends on the horizon include continuing advancements in the fields of artificial intelligence and machine learning and their adoption in the legal world. We can expect further improvements in the efficiency of document and contract review, litigation prediction and social media and communication analysis. For instance, as text analytics technology advances, so too will its application for emerging types of data such as audio and video files. A growing emphasis on ethics, best practices and data protection requirements will rise as the use of artificial intelligence expands. Moreover, legal data scientists of the future may be lawyers of the present. As technology plays a greater role in legal practice, lawyers trained in coding and data analysis may be more valuable to employers . Salient careers might include e-discovery project managers, data analysts and e-discovery programmers and software developers. Lawyers will need to contend with their data science colleagues regarding the process of interpreting and applying statistical analysis. The often-muddy world of the law is generally not a good fit for unqualified quantitative analysis. Simply put, it is easy to find that A is correlated with B. However, it is not so easy to determine which is the cause of the other. In the near future, the work of lawyers, data scientists, business professionals and technology experts will be increasingly integrated as they collaboratively tackle innovative projects for clients. The successful organization of the future will have a holistic team with multidisciplinary expertise working together to solve multi-faceted real-world problems through data-driven approaches.