"Most of the traditional systems companies have used have not been able to draw correlations to other external factors," explains Kilgore. With this new technology, a retailer that traditionally made forecasts based on last year’s sales, for example, can now factor in external variables such as the opening of a competitor’s store a couple miles away. And a boxing promoter looking to predict attendance might run a model incorporating weather and economic indicators along with the prominence of the fighters.

With academic-sounding names like support vector machines, these new predictive intelligence techniques and algorithms are often used together with older approaches, such as regression analysis and neural networks, along with workflow-based mechanisms that allow for human input into forecasts.

"Predictive intelligence requires a fluid combination of multiple technologies," explains Bob Moran, an analyst at Aberdeen Group in Boston. Most of the algorithms search for patterns in large amounts of data. Survival analysis, for example, looks at factors leading up to an event such as an engine failure. Many of the algorithms fit in the broad category of probabilistic or stochastic modeling?they look at the probability that a specific event or combination of events will have an impact on the future.

The new models can often run right on top of an existing transactional system or data warehouse, rather than requiring a separate database and ETL (extract, transform and load) process. Many also claim to be adaptive, or self-tuning, to avoid the need for constant care and feeding from a large professional staff.

New Names and Old

Predictive intelligence software and services come from a slew of startups like DemandTec, Genalytics and Mantas that are focused on particular applications such as financial fraud detection, supply chain demand visibility or retail forecasting. Traditional analytics vendors such as Hyperion, SAS and Teradata also have predictive offerings, as do applications vendors such as Computer Associates, Epiphany, Manugistics and PeopleSoft.

In fact, more predictive analytics capabilities are appearing under the hood of packaged CRM, ERP and SCM applications, where they’re accessible to a broad set of users who previously relied on spreadsheets, gut instinct or a separate analytics department. "What’s new is the packaging of predictive technology in a business process context," says Henry Morris, IDC analyst and vice president of research in Framingham, Mass. (IDC is a sister company to CIO’s publisher, CXO Media.)

The dramatic failure of traditional supply chain analytics to predict last year’s demand downturn has spurred a lot of thinking about how to go beyond just extrapolating from the past. Startups such as eIntelligence, OneChannel and Spotfire tout their ability to combine human collaborative input with advanced modeling to create scenarios for better visibility into the demand chain or other forecasting challenges with lots of unknowns.