Who wouldn’t want to predict the future if they could? Besides the financial benefits that could be realized, for those of us who hate surprises it would be helpful to prepare and plan ahead.

In the world of information science, there is a crystal ball. It is called predictive modeling. A commonly used statistical technique to predict future behavior.

A form of data mining technology, predictive modeling works by analyzing historical and current data and generating a model to help predict future outcomes. Think of the professional gamblers who analyze horse races and predict the winners of future races. This is a perfect example of predictive modeling. Data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available, i.e. more race history.

I live in the bluegrass state where horse racing, especially this month, is a hot topic. Many predictions, scientific or not, are being made on one particular race come May 4th.

In the world of retail and customer relationship management, predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future. Predictive models often perform calculations during live transactions—for example, to evaluate the risk or opportunity of a given customer or transaction to guide a decision.

In the world of risk management, health and life insurers would love to be able to accurately predict secular trends and potential hazards.

When it comes to investments and the stock market, predictive modeling is the bible. The rapid migration to digital products has created a sea of data that is easily available and accessible. This vast amount of real-time data is gotten from sources like social media, internet browsing history, cell phone data, and cloud computing platforms. By analyzing historical events, there is a probability that a business might be able to predict what would happen in the future and plan accordingly.

Other predictive modeling techniques used by financial companies include decision trees, time series data mining, and Bayesian analysis. Companies that take advantage of big data through predictive modeling measures are better able to understand how their customers engage with their products and can identify potential risks and opportunities for a company.

And those wagering in sporting events could learn a thing or two as well.

Melody K. Smith

Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.