Attorneys and data are interacting in unprecedented ways, and it isn’t an overstatement (at least not by much) to say that cases can be won or lost these days on a firm’s ability to deconstruct vast quantities of data. Every day that passes adds to the number of case holdings, legislative bills, and regulations that attorneys must parse through on a daily basis in order to formulate a stance in a lawsuit. And within just a single large case, there may be millions of data points that must be organized and searched through, from emails to browser history to the contents of company computers. Litigation attorneys have seen the era of data analytics coming for a long time now, and it’s safe to say that day is firmly here. Artificial intelligence (AI) is touted as the breakthrough technology that will change the way law is practiced. Many eDiscovery suites and legal research platforms already rely heavily on AI and machine learning, all of which is dependent upon the field of data analytics.
Understanding Data Analytics
Data analytics, also called data analysis, is the process by which data is gathered, organized, and inspected with the goal of discovering useful information. Through the processes of data analytics, patterns emerge and relevant information can be pulled out from hundreds or thousands of documents, like the proverbial needle in the haystack. When machine learning or AI is involved, this is usually taken a step further into the area of conclusions and suggestions as to what to do with the data.
As an example, data analytics can process all transfer of venue orders from a judge for as far back as the data is supplied and turn that into an expectancy of success relative to a particular attorney’s situation. Or, sort through hundreds of thousands of emails in minutes and return only those addressed to or from a particular person. In the world of data analytics, these functions are child’s play. Where the field begins to be truly impressive is when machine learning programs start to perform functions that humans cannot replicate even if we had almost unlimited time. These are processes such as visual representation of data, or the categorizing of millions of data points of metadata for e-discovery responses.
The trend in data analytics is clearly toward more advanced machine learning, including semi- and true artificial intelligence. This advance in the way computers organize and interpret information is going to affect virtually all industries, and legal is no exception. The need of law firms to consume vast amounts of data on a daily basis, and the high-stakes matters routinely at risk, means that the legal industry is ripe to benefit enormously from machine learning capabilities. It’s often noted that since the economic recession of the late 2000s, companies are much less willing to pay top-dollar attorneys to do the work that junior associates can do, or pay junior associates to do the work that paralegals can do. Law firms have sometimes struggled to adapt to this, but with the advent of greater AI capabilities to crunch numbers and documents, the very survival and profitability of many firms will depend on how well they are able to integrate machine learning into their processes and therefore keep a financially competitive edge.
Predicting the Future of Data Analytics
Technology landscapes are changing quickly and the legal industry is in some respects scrambling to catch up, much less keep up. The demand to process and analyze vast tons of data comes at the same time that firms are more budget-conscious than ever before. In addition, clients expect more productivity from the firms that they hire, and for less money than they were willing to spend even ten years ago. So, what does all this mean for the future of data analytics?
In short, it means that the field of data analytics as applied to the legal industry should be expected to grow rapidly. Machine learning and AI are the only practical ways for firms to accommodate and process data in the volumes presented, and that makes data analytics a field that is poised to be extremely needed and very profitable for those that understand it. At the moment, most data analytics programs marketed to law firms are descriptive, which means they are designed to search through data and pull out relevant points of information based upon specific and designated search parameters. However, the future lies in programs that take this one or two steps further, by being able to make logical and intuitive jumps from what a user inputs that they want to what a user might actually want but not know that they want. These are new analytics technologies, and these are the ones approaching AI in function. Predictive analytics predicts future data instead of merely compiling and analyzing past data. And yet another step out is prescriptive analytics, which is a still-emerging technology that looks at past data, predicts future data, and advises the user on specific courses of action.
Data Analytics Startups
The startup field for data analytics companies is robust right now, and new businesses are forming constantly in response to growing market need and a sense that AI is the “next big thing” in technology. Mark Cuban, owner of the Dallas Mavericks and noted entrepreneur, famously announced in early 2017 that he predicts the world’s first trillionaire will be an artificial intelligence entrepreneur. Here are three data analytics companies breaking new ground.
Brainspace – applying data analytics to e-discovery problems in order to shave dollars and time off a law firm’s case expenditures.
Knomos – this Canadian legal research company is using advanced data analytics to analyze case law holdings and map them into 3D visual representations, allowing its users to visualize connections between relevant data in a new way.
Intraspexion – using predictive analytics to analyze intra-company emails and catch misconduct before it escalates.