Predictive Analytics: Business Intelligence's Next Step

Real-time information, once a competitive differentiator that produced more timely and relevant business decisions, is now a commodity. Even midsize companies process transactions as fast as the New York Stock Exchange, while decision makers communicate and collaborate over broadband networks as if they were in the same office. Sheer speed isn't the advantage it once was.

So what's next? What's next is what's next--the ability to forecast where events are heading, then make informed decisions based on that assessment. Predictive analytics, the scientific name for using a data warehouse as a crystal ball, is where business intelligence is going. It involves running historical data through mathematical algorithms--neural networks, decision trees, Bayesian networks--to identify trends and patterns and predict future outcomes. Will product demand surge? Will a patient relapse? Will a customer take his business elsewhere? Our ability to make such educated guesses is key to improving service, cutting costs, and exploiting new market opportunities.

Blue Cross Blue Shield of Tennessee now predicts the health care resources postoperative patients will need years down the road. The Federal Aviation Administration is identifying links between pilot health conditions and aviation accidents, with an eye toward avoiding them. FedEx anticipates which customers are most likely to respond to a new service or defect to a competitor.

The idea isn't new. Insurance companies have used actuarial tables for decades to predict how long policy holders will live or the likelihood of their getting into a car accident. Financial firms have used predictive analytics to assign credit-risk scores to borrowers.

What's different now is that vendors are building predictive analytics into mainstream applications for everyday decision making by all types of employees. IDC expects sales of predictive analytics software to grow 8% annually, to $3 billion by 2008.

Startup TrueDemand Software has developed supply chain applications that use data from radio-frequency identification systems to help retailers and manufacturers predict product demand and optimize inventories. Atrenda makes software that uses predictive analytics to verify early in development whether a semiconductor design will meet its specifications.

IBM last week introduced an inventory management application for retailers that uses built-in predictive analytics and replenishment rules to monitor product inventory, develop safety stock, and recommend orders based on an analysis of historical demand. The commercial app has been used by IBM consultants for years.