The Value of Real-Time Data, Part 2
Previously, predictive analytics was summarized as “system anticipates” (http://profreynolds.wordpress.com/2011/08/31/the-value-of-real-time-data/). But that left a lot unsaid. Predictive analytics is a combination of statistical analysis, behavior clustering, and system modeling. No one piece of predictive analytics can exist in a vacuum; the real-time system must be statistically analyzed, its behavior grouped or clustered, and finally a system modeled that can use real-time data to anticipate the future – near term and longer.
Examples of predictive analytics in everyday life include credit scores, hurricane forecasts, etc. In each case, past events are analyzed, clustered, and then predicted.
The result of predictive analytics is, therefore, a decision tool. And the decision tree will, to some degree, take into account a predictive analysis.
The output of Predictive Analytics will be descriptive or analytic – subjective or objective. Both outputs are reasonable and viable. Looking at the hurricane predictions, there are analytical computer models (including the so-called spaghetti models) that seek to propose a definitive resulting behaviour; then there are descriptive models that seek to produce a visualization and comprehension of the discrete calculations. By extension, one can generalize that descriptive predictions must be the result of multiple analytic predictions. Perhaps this is true.
Returning to the idea that predictive analytics is comprised of statistical analysis, clustering analysis, and finally system modelling, we see that a sub-field of analytics could be considered: reactive analytics. Reactive analytics seeks to understand the statistical analysis, and even the clustering analysis, with an eye to adapt processes and procedures – but not in real-time. Reactive Analytics is, therefore, the Understanding portion of the Data-Information hierarchy (http://profreynolds.wordpress.com/2011/08/31/the-data-information-hierarcy-part-3/). Predictive Analytics is, therefore, the Wisdom portion of the Data-Information hierarchy.