Law firms, their number of lawyers and mentions in court documents

Data analytics and visualization can sometimes tell you about where your law firms stands relative to competitors and can therefore guide your positioning and selling efforts.   An illustration of this benefit comes from Corp. Counsel, Oct. 2016 at 44, where a table shows counts of U.S. law firms that “turn up the most in court documents.”

The table lists the firms by number of “mentions”; most often mentioned were Littler Mendelson and Ogletree Deakins at 100, least often mentioned were Fisher & Phillips, Gordon Rees, and Hunton & Williams at 24.

A more revelatory analysis matches the number of mentions of a firm to its number of lawyers.   After all, bigger firms are more likely to represent litigants than smaller firms, everything else held constant.

corpcounsel-mentions

The plot above shows data on law firm lawyers from a year or so ago, but the relative size differences among this group of 30 likely still hold.  It shows each firm’s lawyer count from the left axis and its mentions from the bottom axis, with the firm name next to its point on the scatter plot.

One possible conclusion from this plot is that firms specializing in employment litigation rack up the most mentions.

Analytic software requires curating before it can proceed reliably

A column in Met. Corp. Counsel, Sept. 2016 at 21 by David White, a director at AlixPartners, starts with three paragraphs on the severity of anti-corruption risks to U.S. companies that do business abroad and the associated huge variety and volume of documents and records that might have to be analyzed to respond to regulators.  Data analytics to the rescue!

White continues: “Unlike their traditional counterparts, newer analytic systems based on big data technologies, predictive analytics and artificial intelligence are not bound by upfront data transformations and normalization requirements …” [emphasis added].

In my experience, analytical software inevitably requires the data fed to it to be formatted cleanly in ways that it can handle.  For example, dollars signs can’t be included, currencies need to be converted, dates need a consistent format, missing numbers or problematic outliers need to be dealt with – these and other steps of “upfront data transformations and normalization” are often manual, require human judgment, and can take hours of thoughtful attention.  Moreover, the data doctor ought to keep track of all the steps in the clean up.