Welcome to the fifth installment in our blog series on firmwide analytics. Ask anyone who has attended a legal conference recently, and they will tell you that data is everything. From AI applications to machine learning, it all hinges on information. However, there seems to be less certainty about exactly what data everyone should be collecting. Or why for that matter… For this very reason, we at Clocktimizer will be looking at the full range of data analysis available to firms. We will be breaking down firmwide analytics by department and exploring the data to collect, the insights you can hope to gain from it, and how you can apply these learnings in your firm. 

This installment is the first to be released in 2020. With that, we will be taking the opportunity to look to the future of firmwide analytics. How will they change the way we practice law? After all, it has already been a momentous decade for legal technology. It began with the New York Times informing the world that the age of the robot lawyer was upon us. Ten years later (and having reached over 1 billion dollars in investment), it’s clear that this isn’t the case. However, legal analytics has nonetheless caused a momentous change to how law firms function. We look at where they are now, and where they could take us.

The current world of legal analytics

Legal analytics (often called AI, but in some cases only in the loosest sense of the word) is a growing field. The vast majority of it exists to support and improve the efficiency with which lawyers manage the practice of law. According to Emerj (who have spent some time mapping this ever growing field) current legal AI falls into one of six major categories:

  1. Due diligence – Litigators perform due diligence with the help of AI tools to uncover background information. This includes contract review, legal research and electronic discovery in this section.
  2. Prediction technology – An AI software which generates results that forecast litigation outcomes.
  3. Legal analytics – Lawyers can use data points from past case law, win/loss rates and a judge’s history to be used for trends and patterns.
  4. Document automation – Law firms use software templates to create completed documents based on data input.
  5. Intellectual property – AI tools guide lawyers in analyzing large IP portfolios and drawing insights from the content.
  6. Legal project management and billing  – Lawyers’ billable hours are computed automatically and their data can then be analysed and used to manage budgets, fixed fees and project management

Edgar Allen Rayo, Emerj

How do analytics currently support legal practice?

So what do these developments have in common? To return to the New York Times article, what they do not seem to be doing is replacing anyone. Arguably, due diligence software has vastly improved the lives of first and second year lawyers globally. They may now be logging less hours under due diligence, but clearly there are plenty of other ways to fill their time. The same can be said for the advances in analytics and document automation, prediction technology and legal analytics itself. What analytics seems to be doing for the practice of law is twofold. It is looking to reduce avoidable human error. And it is looking to increase efficiency.

Repetitive work pervades legal practice. And where repetitive work exists, so does the likelihood of errors due to boredom, or a host of other facts. Repetitive work is also exactly what technology was designed to address. As such, the most successful firms are currently using AI to reduce this impact. Be it by analysing whether a judge is likely to be friendly to your arguments, or by drafting 200 contracts for you.

The current limitations on analytics and AI

While AI is now relatively pervasive in legal practice (most notably in e-discovery) it still faces challenges. AI (and we are aware that this may be controversial to say) is statistics on steroids. Computers are not intelligent, in and of themselves. They are good at reducing repetitive work, because they are excellent and recognising and replicating patterns in data. For this very reason, legal analytics is currently held back by the quality of the data available to it. 

In order to maintain competitive advantage the majority of firms do not share data. Furthermore, much of the data that is collected is not in a form that is useful to a computer. Take the growing world of predictive technology. Predictive technology, to a certain extent, relies on the analysis of previous case law to determine the likelihood of success of the current matter at litigation. However, case law comprises reams and reams of plain text. Judgements, arguments, testimony. All of this incredibly valuable information is unable to be analysed by a computer. Which words should the computer find important? Which shouldn’t it? This must all be taught and doing so is a complex task. Efforts are rapidly increasing to make this sort of information easily analysable by machines but we are only at the tip of the iceberg. Accordingly, the sophistication of the analysis is held back by the relative lack of data. 

For law firms looking to the future

In light of this, law firms looking to take greater advantage of analytics to support their legal practice should pay close attention to these trends. A whole host of analytics tools already exist to streamline your existing processes. Consider then, what other repetitive tasks are still able to be automated? Alternatively, what siloes of data is your firm currently sitting on? Is this data in a usable format? If it were, what could you learn from it? 

It is a recognised phenomenon that human beings overestimate the impact of technology on our immediate future (we’re looking at you blockchain) and underestimate its impact on our distant future. To that end, the skill in ensuring your firm’s analytics are going to meet the challenges of the future is in the preparation. Have a strategy, collect good, structured data, and keep yourself informed about the advances in the ways that data can be analysed.

Next week

In light of the importance of collecting, understanding and analysing data in firms, the rise in importance of the Knowledge Manager should be no surprise. Next week, we look at how KM supports and informs firmwide analytics and offer practical tips for firms looking to implement this role.