Editor’s note: This article is by Christer Johnson, IBM Global Business Services’ Advanced Analytics Services Leader for North America. A member of Christer’s team, Ryan Hendricks, will participate in a panel “Big Data in the Sports Industry” at the IBM Research Colloquium “Box Office to Front Office: Winning with Big Data” on August 10, 2012. Watch over livestreambeginning at 10 a.m. U.S. Pacific Time.
One of the many things I’ve learned from more than 19 years of using analytics to solve challenging business problems is that the word analytics means different things to different people. So before diving into numbers, I define analytics by the objectives they intend to achieve, and the decisions they intend to improve or accelerate. In that context, analytics falls into three categories: descriptive, predictive, and prescriptive.
Descriptive analytics, also referred to as business intelligence, provide a clear understanding of what has happened in the past, through visualization of key performance metrics or other data in a report or dashboard. Today, the past can be as recent as just a millisecond ago.
The sports world has long been a leader in the use of descriptive analytics to provide fans, coaches, and players with a wide range of statistical reports that help them understand what’s happening on the field — whether a coach wants to improve play, or fans want to win their fantasy league.
However, with descriptive analytics, fans and coaches alike must rely on their intuition and ability to interpret the data in order to gain any insight on the relationship or correlation between data inputs and data outputs.
That’s where predictive analytics, the second category of analytics, comes into play.
In predictive analytics, the objective is to use advanced mathematical techniques on that past data to understand the underlying relationship between data inputs, outputs and outcomes. Effective predictive models let us quickly understand and estimate outcomes across a wide array of scenarios and conditions. Commonly used for forecasting, simulation, root cause analysis, and data mining, predictive modeling techniques provide insight into complex data that we can’t manually interpret from a report or interactive dashboard.