Descriptive statistics and the step beyond to predictive statistics

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).  “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 statistics, as some call it, include calculating averages, medians, quantiles, and standard deviations.  These summary statistics, yet another term for the basic calculations, are themselves simplified models of the underlying data.  [Note that a “statistic” is properly 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 open-source R and its lm package, you can easily fit a 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 derive numbers, not just describe them

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