ABSTRACT
In a broad sense almost all the subjects have some overlap with analytics. For example, machine learning is the backbone of predictive analytics, where we are trying to use data to deem the likelihood of an unseen event. Another example is Visual business intelligence, when data is turned into dashboards or what many term visual analytics. Another example is a functional form of analytics, for example, “healthcare analytics.” To illustrate some difficulty in trying to categorize types of analytics, take “text analytics” as an example. Text is a type of data, so it could be a category of its own. Alternatively, it could be that we want to determine the author of a text. Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics. This framework is not comprehensive, nor completely accurate, but it is useful.
