Descriptive analytics compared to predictive analytics

A fundamental distinction between two kinds of data analytics appears in a report published by KPMG, “Through the looking glass, How corporate leaders view the General Counsel of today and tomorrow” (Sept. 2016).  The report observes that “Companies are making greater use of data analytics and are increasingly moving from descriptive analytics (where technology is used to compress large tranches of data into more user-friendly statistics) to predictive analytics and prescriptive models that extrapolate future trends and behavior.” (page 14).

Law firms and law departments can avail themselves of many kinds of software to summarize aspects of a data set.  Descriptive analytics, as some call it, include averages, medians, quantiles, and standard deviations.  These” summary statistics,” yet another term for the basic calculations, are simplified models of the underlying data.  Note that a “statistic” is a number calculated from underlying data.  So, we calculate the variance statistic of all this year’s invoices where the underlying “raw” data is the data set of all the year’s invoices.

Predictive statistics go farther than descriptive statistics.  Using programs like R and the lm package, you can create a linear regression model that predicts the number of billable hours likely to be recorded by associates based on their practice group, years with the law firm, gender and previous year’s billings, for example.   Predictive analytic models allow the user to forecast numbers.

Limited interviews fall short of “data”; glimmers of awareness of machine learning

Two observations arise from a report published by KPMG, “Through the looking glass, How corporate leaders view the General Counsel of today and tomorrow” (Sept. 2016), one about what constitutes “data from a survey” and the other about dawning awareness among general counsel of data analytics.

Regarding the first observation, the report states that its conclusions are based on interviews with 34 “CEOs, Chairmen, General Counsel and Heads of Compliance who made themselves available for interviews and kindly agreed to participate in our research.” (pg. 27).   While you can certainly identify themes from interviews, unless you ask everyone the same question (or some questions), you can’t quantify your findings.  Writing that “risk management is top of mind for GCs” is worlds apart from writing that “Twenty-six out of 34 interviewees mentioned risk management as a significant concern.”  Additionally, surveys are designed to gather data that is representative of a larger population.  It is unlikely that the particular group of 34 who agreed to speak to the KPMG interviewers are representative of the broader population of global CEOs, Chairmen of the Board of Directors, General Counsel or Chief Compliance Officers.  Subjective interpretations of what a limited group of people say falls short of quantified research, although those interpretations have whatever credibility a reader assigns them.

The second observation highlights the passing reference — but at least it is a reference — to machine learning software becoming more known to general counsel.  “Technology was also cited as an important tool to help the GC improve efficiency, at a time when they are continually being asked to do more with less: ‘New technology helps the GC to be more responsive to the real-time demands of the C-suite of executives,’ says the CEO of a large consumer services company. Companies are making greater use of data analytics and are increasingly moving from descriptive analytics (where technology is used to compress large tranches of data into more user-friendly statistics) to predictive analytics and prescriptive models that extrapolate future trends and behavior. The Office of the GC is being transformed by this process, for example, when performing due diligence on M&A targets or monitoring global compliance.” (page 14).  The following sentences direct attention to predictive coding in e-discovery, it is true, but at least the report links awareness of predictive analytics to transformation of law departments.