Primer on descriptive, prescriptive, predictive analytics

I found the reading this week–beyond the people analytics one–to be a bit lacking on what we’re talking about on Monday. So I aggregated some items that better define descriptive, prescriptive, and predictive.

The semi wonky textbook definition of the flavors of analytics go like this:

• Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. … Diagnostic analytics is a deeper look at data to attempt to understand the causes of events and behaviors.
• Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation. Prescriptive analytics is related to both descriptive and predictive analytics.
• Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.

But this post explains it a bit better.

• Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
• Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
• Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?

As does Quora.

And finally, this post from my site has a handy Gartner chart that sums of the continuum. Most companies are in the descriptive and diagnostic phase of analytics and trying to get to the predictive and prescriptive part (think Watson, AI etc) where there will be some technology/model/AI that tells you what will happen and how to act.