Does Legal Analytics Really Need “Big Data” to Make Predictions?

• Author: Mona Datt
• Date: October 06, 2016

If you’re at all interested in legal technology, you’ve probably grown tired of the recent influx of fear-mongering articles about “robot lawyers” that are going to put legal professionals out of a job. This sub-genre of legal tech reporting features a lot of buzzwords. There’s “machine learning”, “NLP (natural language processing)”, “AI (artificial intelligence)”, and “predictive analytics”, to name just a few. Regrettably, a lot of these articles discuss legal technologies only in very vague terms, or sometimes don’t bother with definitions at all. And it’s very difficult to have a nuanced conversation about legal tech when it seems like everyone is talking past each other and no one can agree on the basics. So I’d like to go back to the beginning and start with something foundational: what actually is legal analytics?

In an InfoWorld article from 10 years ago, Jeremy Kirk bemoaned the fact that the word “analytics” was used so broadly that nearly everyone seemed to have a different idea of what it meant. Kirk describes how Gartner, an information technology research company, had put together an informal survey asking its users to define the term. The responses “were completely all over the place” and left Gartner with “more questions than answers”. In the decade since that article was published, the situation has improved — but not by much.

After seeing the results of that survey, Gartner put together a working definition of analytics that I think is still very useful today:

“Analytics leverage data in a particular functional process (or application) to enable context-specific insight that is actionable.”

It’s a little bit dense, but what I find most helpful about that definition is that it highlights two of the most important components of analytics: that it gives you information about data that exists in a particular context (like the law) and that it’s information you can use for something.

Here’s a very basic example of analytics in action. If you’re reading this article on Slaw, you’ll see a section called “The Count” at the top of the right-hand menu. As I’m writing this, it’s telling me that there are 14,156 posts on Slaw and 18,449 comments. This is analytics in its simplest form. It takes a body of data (Slaw posts) and uses metrics to illuminate a context-specific aspect of that data that wouldn’t otherwise be obvious. These numbers are also useful. As a reader, they give me a rough sense of Slaw’s size: it’s not The Globe and Mail, but it’s...