I love my Fitbit. It tracks all sorts of things — steps, calories, exercise, floor-climbing, among others. It then visualizes the gathered data and presents it to me in a way that allows me to take action based on that information. I can make different decisions based on real information. Whether I actually make the right decision is on me, but I have actionable data upon which to rely.
And, ok, I like the competitive aspect as well.
Yes, we live in a data-driven age — and Lean Six Sigma was certainly ahead of its time in its affinity for data-informed decision-making. You cannot venture too deeply into that world without being engulfed by arcane terms, formulas, tests and discussions of data capture.
In our industry these days, we hear constantly about “big data” and the powerful insights that can now be drawn from our ability to process terabytes of data. It is quite easy to become overwhelmed by the wave of data analytic acolytes, whose scope and scale of vision seem provocatively incredible by intent:
“[b]ig data in general, and predictive data analytics in particular, are the potential holy grail in the practice of law.”
Donald Wochna, Legal Technology News
So, what’s a lawyer to do?
After all, most lawyers will admit cheerfully that they “don’t do numbers,” and some will even tell you that they got into the law game specifically to get away from math. Do not despair, my friend. It does not have to be that complicated. When we say we use data to drive decisions, the Fitbit example is what we mean. Manageable data, analyzed simply, and presented clearly in a way that allows us to make better decisions.
Data analytics matter for a new and improved legal industry. Basing decisions on actionable data leads to better, more fundamentally sound decision-making (particularly for business-of-law decisions, but in some cases even in practice-of-law decisions).
Our experience teaches that, done correctly, data analytics helps lawyers in two main ways:
- first by leading to new flashes of insight (you will find trends that are surprising and discover new ways to look at old problems);
- and second by quickly validating or in some cases eliminating potential courses of action that hinge on assumptions (you will save time and money in course-correction).
We have found that there is also a third, important value of data-informed approaches — one that is particularly relevant to dealings with lawyers: the right data tends to narrow the scope of the inevitable arguments. Note, I did not say eliminate. I said reduce. No one is going to keep lawyers from arguing.
As more reliable information can be produced about the process or outcomes, however, the scope of the argument — and along with it both the problem space and the solution space, as our mathematician and design thinking friends would add — will narrow. It will inevitably move from an argument over the existence of a problem to a discussion about the problem itself and ultimately progress toward possible solution sets.
Trust me, if you get there, it is enormous progress.
Understand What’s in Your Way
So, what are the main challenges?
Math intimidates many lawyers. As previously mentioned, lawyers typically do not relate well to numbers. Our stock-in-trade is language. So, we react negatively to the arcane discussions of statistical analysis typically found in Lean Six Sigma analysis. This can be resolved by the increasing democratization of data: simple applications of visualization techniques can help dispel math-phobia and help lawyers find their inner data geeks. Good dashboard design takes the math out of the data by aggregating, synthesizing and interpreting the numbers — and zeroing in on the insights.
The goal is to de-mystify the data analytics process; remove the jargon and produce information in a way that makes sense to lawyers.
Good data is rare in legal organizations. The more difficult challenge is that reliable data is very hard to come by in this industry. One of the first problems we encounter in working with legal departments is the lack of reliable or usable data. This makes sense since lawyers historically have not relied heavily on data analysis. Data is certainly maturing around the pricing of legal services because buyers are starting to use that information. In other areas, however, lawyers have shied away from using objective data (or they use anecdata, which may be worse).
Thus, they have not found it essential to develop accurate data sets — the proverbial chicken/egg problem.
Similarly, useful public data focused on key decision points in areas like litigation is difficult to find. The potential is clear — just look at Lex Machina in the IP space or the work the Stanford Securities Litigation Analytics (SSLA) project is doing. Broadening this construct across other types of cases — where settlements are confidential and automated access to key data is rare — is quite difficult.
There are tremendously interesting things being done in the world of data analytics in our profession but do not understate the challenge and cost in gathering the amount of reliable data needed to produce actionable information.
The legal function is seen as a cost center. In most instances, we find that legal departments have to fight for resources — particularly technology or similar support resources. Cleaning up historical data usually requires such resources, and a fair amount at that. In order to change this dynamic, we need to help the underlying businesses recognize the strategic value such investments into the law department could bring to the enterprise as a whole.
Of course, demonstrating that value usually requires — you guessed it — data, and so we encounter yet another chicken/egg problem.
So, make it simple and start small. Start by counting stuff. I mean that literally. Count stuff. Legal organizations may have big data problems but they are not yet Big Data problems. Instead, think of it as the creation of Small Data, or Tiny Data. We do not need massive processing power and an army of statisticians. Legal practice data is a different beast. To get started, we need core information around the practice mechanics, outcomes and results.
In our experience, the exercise of counting things forces the raising and answering of the most important questions. As the saying goes, you manage what you measure — and you want to manage what matters. So, we want to measure what matters. Counting things means figuring out what matters. Is it speed to resolution? Count cycle times and dispute durations. Is it risk management? Count the number of lawsuits (or go further upstream and count administrative charges, and internal investigations, or complaints to HR).
Once you start counting, you can then get to comparisons (benchmarking against historical or industry baselines) and to binary treatments (are we hitting a pre-set goal or not?). Then look for patterns by ordering the numbers (are there hot spots?) and look for correlational trends (are there linear or inverse relationships?). Beyond these descriptive analyses lies a whole horizon of possibility: risk mitigation and predictive analytics. It will take time to build up a valid set of data to attempt these data applications but you will never get there until you start.
The key is to devise a centralized strategy that can produce a return commensurate with the time and effort investments necessary to keep the process going.
The long-term possibilities of data analytics — advanced decision support and access to predictive analytics — are too significant to ignore; as the legal industry matures as whole in its handling and usage of data, third-party data pools will become more accessible and useful. Until then, we need to deal with the realities of the here and now to both produce useful, current data as well as to support and lead into the opportunities of the future.
The immediate benefits — enhanced visibility, the ability to measure operational performance of the legal organizations — should serve as the low-hanging fruits that merit the startup costs of beginning a data hygiene and management program.
The important point is that you have to start. Otherwise, you will never get anywhere. So get up and walk around the block — otherwise, you will never hit your steps target.