Just because we do something all the time does not mean we are good at it. The internet, for example, has recently informed me that I don’t know the right way to tie my shoes, use a kitchen knife, or eat Chinese takeout. I’ve been doing these things most of my life. But I have not been doing them well.

With knowledge and practice, I could.

There are, however, things that I could never do, let alone well. Many of them involve the aggregation and analysis of large quantities of data. I could never read, write, organize, and cross-link 5,193,677 English-language articles in an attempt to capture all human knowledge (Wikipedia). I could never index 60 trillion web pages, let alone keyword-search and rank them by relevance 3.5 billion times per day (Google). I could not route 205 billion messages per day, let alone filter out the 45 percent that are spam (email). Humans create, consume, and curate much of this content. But it is not possible to organize, analyze, and deliver in a useful way without machines.

Rather than worrying about if or when the machines are going to replace us in the future, it is incumbent upon us to find the areas where the machines can supplement our abilities today. Sometimes, this means automating that which we were performing manually. But often times, it means doing that which was not previously feasible.

I’ve reviewed many legal invoices. I thought I was pretty good at it. I was wrong. The scales first fell from my eyes a few years ago when I applied analytics to billing data. I was shocked to find several timekeepers who were de facto full-time employees of the law department for which I was doing spend analysis. The timekeepers rarely billed more than a couple of hours per day to a single matter. But they were billing across several matters per day with the net result being that they consistently billed the client more than seven hours per workday. And some of them had been doing so for years.

The bills may very well have been righteous. The attorneys could have been dutifully recording time dedicated to the matters assigned. But the steady volume screamed out for a discussion about unbundling, insourcing, or, at the very least, alternative fees. Yet, no one had been inclined to have such a discussion because the information was obscured by the data.

One handy working definition of information is data organized in a way that is useful for decision-making. Traditional legal invoices are data rich but information poor, in part, because of the way the data is presented. Many in-house counsel review invoices line-by-line, matter-by-matter, month-by-month. This pixel-level view blurs the bigger picture. How would someone presented with such a granular perspective become aware of larger trends? How would they be alerted to the fact that someone billing across multiple matters is a de facto employee? They wouldn’t.

This limitation is not just a personal shortcoming. It’s a human shortcoming. Google itself has expressed frustration with the approach-induced myopia of traditional invoice review in describing the impetus behind their internal Outside Counsel Dashboard. They designed and built their own system for surfacing insights that simply aren’t discernible at the individual invoice level. The limitation to be overcome is not one of talent or intellect but raw capacity in dealing with large volumes of data. Humans do many things well. Analyzing large data sets without machine assistance is not among them.

The term “analytics” gets thrown around quite a bit. For too many lawyers, it has the flavor of technobabble that might as well come from the Jargon Generator. But analytics is simply finding meaningful patterns in data. Discerning when there is likely to be a line at the coffee machine is analytics—i.e., a meaningful patterns (regular, predictable spikes) emerge from the assembled data (line length cross-referenced with time). Human beings are naturally inclined to look for and interpret patterns. But we are limited in the amount of data we can collect, organize and process. That is where the machines come in.

I could not review invoices for anything other than the content on the page because I did not have the distance and perspective necessary for meaningful patterns to emerge. At least I was still superior to machines in my ability to read between the lines of individual narratives and use my experience/intuition to judge appropriateness of the associated time entry. Oh wait! It turned out I was quite bad at that too. Enter the algorithms, a subject for next post.

D. Casey Flaherty is a consultant at Procertas. Casey is an attorney who worked as both outside and inside counsel. He also serves on the advisory board of Nextlaw Labs. He is the primary author of Unless You Ask: A Guide for Law Departments to Get More from External Relationships, written and published in partnership with the ACC Legal Operations Section. Find more of his writing here. Connect with Casey on Twitter and LinkedIn. Or email [email protected].