Data Lawyers and Preventive Law

This article originally appeared in Legaltech News on October 25, 2012.


By Nick Brestoff

I fondly remember Louis M. Brown (1909–1996), a University of Southern California professor of law who advocated for and arguably pioneered the concept of "preventive law." His philosophy was this: "The time to see an attorney is when you're legally healthy—certainly before the advent of litigation, and prior to the time legal trouble occurs."

He likened his approach to preventive medicine. For example, when he was president of the Beverly Hills Bar Association, he launched a program to give free legal advice to young couples before they were married, much like how doctors give health advice to prevent disease or promote optimal health.
Now that analogy shows the way to where our legal profession must go. Shouldn't we attorneys help maintain the legal health of our clients? Yes, but we cannot get there on our own. We swim comfortably in language, but we must recognize that words inside a computer are digital—reduced to their binary form. Lawyers need to see that there's a future in spending a little quality time with hard data and the IT folks. With the advent of Big Data and Big Data technology corporate counsel now have a better opportunity to prevent illegal acts than they did when they were working with business and records managers.

Now, if you're an attorney, let me take you out of your comfort zone. In today's world of big data, experts speak of a burgeoning need for "data scientists" to get a handle on Big Data, which is both structured and unstructured and voluminous. According to IBM Corp., 90 percent of all the information ever created was created in just the last two years. Data scientists are already busy conceiving and deploying ways to use that onrush of information. They speak of "predictive analytics," which is not at all the same thing as the "predictive coding" of which we speak in the e-discovery space.

In e-discovery, we use "predictive coding" to assist us in the arduous task of splitting a document set into subsets, such as "not relevant," "relevant," or "relevant but withheld as privileged." Though predictive coding and predictive analytics look and sound alike, they are not even kissing cousins. Predictive analytics typically involves a search for patterns, with a goal of being able to predict future behavior.

It is this capacity to predict behavior that makes predictive analytics attractive to an enterprise in its ever continuing effort to generate revenue. For example, the financial industry puts predictive analytics to work every day, and we see the results in our credit scores. And predictive analytics recommends purchases for us when we order products online. For example, online shopping sites such as Amazon have recommendation systems that inform us of what others have purchased in addition to our recent order.

While recommendation systems are babies compared to credit scores, they are improving and making it clear why companies are hiring data scientists. For example, by mining the social media for comments to "friends," the data scientists are helping retailers to optimize revenue.

Unlike data scientists, lawyers are concerned not with customers but with clients. When it comes to clients, our job as litigators is, in part, to advance their interests and, in many cases, to protect them. But there are many ways to protect our clients' interests. We don't always have to be on one side of a case caption as plaintiff or defendant. Why wait until after the lawsuit is filed to protect clients? As lawyers, we could learn from "business intelligence" that drives retail revenue and use technology in a way that may be as revolutionary as when legal research engines replaced book stacks. My conviction is that there is room in the world now for "data lawyers."


Put Brown's teachings and the concept of preventive law into the context of law and Big Data. With Big Data, new search techniques and powerful computers could mine ever-growing enterprise data for the factual beginnings of a lawsuit. As lawyers, we can train systems to spot the facts that may result in legal issues whether those facts reside in customer complaints, employee complaints, the factual allegations in lawsuits against our clients, or in lawsuits filed against any competitor in the same SIC code.

Brown would want "data lawyers" to spot the facts that could mature into lawsuits. He would even want us to frame those law school favorites, hypotheticals. Brown would have us investigate and analyze data to reduce litigation risks caused by bad facts that might give rise to a lawsuit.

Data lawyers could use Big Data to avoid lawsuits altogether, which also means they can help to avoid a rather large set of costs: the fees paid to outside counsel, the costs of e-discovery when litigation happens, the regulatory penalties, and the damages awards. All of that avoided.

Think of it. Prevent one death, one really serious injury, one major investigation, or one class action—even a small class action—and such a system, operated by a data lawyer practicing preventive law, will be adding a great deal of value to an organization.

One could therefore argue that, like data scientists, data lawyers will also help to make companies more profitable. We would just do so in a very different way. Data scientists may be thinking about how to use Big Data in the service of privacy, security, and higher net revenues. The data lawyer would be working to limit the costs that eat into those hard-won revenues. In other words, data scientists and data lawyers work on different ends of the profit margin—together they would widen the profit margin for the enterprise.

The data lawyer would work on metrics such as the number of lawsuits, the amount of fees paid to outside counsel, the spending for e-discovery, and the amounts paid out in damages, all of which should go down. In fact, the metric that might go up is the number of in-house data lawyers, and with good reason.

Besides saving companies lots of money, data lawyers can also help to find the distractions that sap productivity and signify dysfunction. To even find a dysfunction is to take a very large first step towards helping business executives address it. When they do, and boost productivity, companies will be able to be more price competitive, which is good for consumers, too.

Data lawyers can also help protect executive decision-makers, because by actively trying to prevent litigation, the efforts of in-house-counsel-turned-data lawyer will go a long way towards negating any specific intent on the part of the executive and the enterprise to do harm, be it physical, financial or fraudulent. A data lawyer will not only strive to achieve the dollar values associated with cost-avoidance, but will also provide an advice-of-counsel defense as well.

So my prediction is that one day preventive law will begin to enjoy a come-back. We've forgotten, amidst all the battling we do, that the enterprise would be better off avoiding litigation wars than fighting them.

So I hail big data—it enables preventive law. Big Data and preventive law will allow us to move the reasons to search Big Data to the earliest point in time—to the time of information management. That's where we should put our best efforts. Let's start thinking about using predictive analytics, with prevention in mind, to reduce the risks of litigation before a lawsuit is filed.

Will all this happen overnight? Hardly. According to Craig Ball, trial lawyer and e-discovery special master, there needs to be a reason for the "Homo Erectus" lawyer to evolve into "Homo Electronicus" and for lawyers to take the evolutionary step toward data lawyers. But the trigger for this change will not be the admonition that attorneys will just have to re-engineer themselves to stay current or avoid malpractice. The situation will likely only change when the clients realize what data lawyers (i.e., in-house counsel) can bring to the table—value in cost and risk reduction.


Reprinted with permission of LEGALTECH NEWS.

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