Descriptive Analytics, or Harnessing the Power of Data by Ana-Iulia Alexandrescu

As we've seen, the classical definition of analytics breaks down the category into three: descriptive (or what happened/is happening), predictive (what will/might happen), and prescriptive (what to do to get the desired outcome). Whereas predictive and prescriptive analytics fall under the category of advanced analytics and require expert knowledge to develop reliably, descriptive analytics are probably more prevalent than we know. Anyone who has ever analyzed a data set using Excel or looked at plots to try to determine trends and characteristics has done descriptive analytics of some sort.

However, there are more sophisticated and interesting tools to make sense out of data, and the business intelligence community is promoting their use extensively. On Wednesday morning, Ugo Sglavo from the Advanced Analytics and Optimization Services R&D group at SAS gave a brief overview of the different tools that are available for users interested in transforming data into information. Beginning his talk with the definition of the term "apophenia," which represents the technical term describing seeing meaningful patterns in the data, he gave a plethora of examples ranging from marketing, finance, voice recognition, and text analysis applications to illustrate the different techniques one might use to describe the patterns in the data.

He talked about visualization as a tool to characterize products for up to five dimensions; network graphics to develop better, more meaningful ways of representing graphs and networks; segmentation clustering as one can apply in marketing to group customers; associations such as those used by Amazon to perform market basket analysis; and text analysis as the "dark matter" of IT. Sglavo provided a good overview of the different tools available to perform better descriptive analytics and amass the hidden value of the data we are beginning to be obsessed with gathering.