Traditional business intelligence software does a very good job of showing you where you’ve been. How many items did you sell yesterday? To whom? What were the margins?

Factoring in other variables

This information helps with forecasting. Based on past demand, you know how many products you can expect to sell in the same time frame in the future. However, forecasting only goes so far. Factoring in other variables – market forces, political upheaval, changing customer preferences, etc. – is trickier, yet this is what the brave new world of predictive analytics promises to do.

Predictive analytics features

The big business intelligence software vendors have begun rolling predictive analytics features into their main software suites. The same organizations leading the BI market – SAP, SAS Institute, IBM and Oracle – are also the ones paving the way for predictive analytics.

Everyone makes predictions

To put it simply, key elements of predictive analytics have already been proven. Take traditional business intelligence, combine it with data mining and add on statistical analysis and you have predictive analytics. Math geeks will squabble over the nuances, say, whether a specific model is a predictive, descriptive or decision-making one, but for most organizations this boils down to using historical data along with probabilities to better assess the future.

Watching a baseball game

Arguably, humans make their way through the world on the basis of predictive analytics. Skipping the interstate for surface roads because of heavy traffic is a simple version of predictive analytics. Watching a baseball game is an ongoing lesson in predictive analytics. In a hitter’s count, you expect the pitcher to throw a fastball. A lot of information actually goes into that prediction, and the statistics back it up.

Baseball is known as a stat geek sport for a reason. The trouble is that the game of baseball has many restrictions on it. It isn’t an open-ended system.

More sophisticated models are needed

Most organizations need more sophisticated models for such complicated things as customer retention, supply chain management and the development of new product lines. And the more complicated a prediction becomes, the more risk there is for garbage-in-garbage-out statistics.

Using Social Media to Glean Predictive Analytics

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