RESEARCH & RESOURCES

LESSON - How Organizations Benefit from Predictive Modeling

By Rado Kotorov, Technical Director of Strategic Product Management, Information Builders

Recently, there has been a surge in the use of predictive modeling and scoring applications. Contributing to this trend is the fact that today’s transactional systems capture massive volumes of complex data, with certain elements having as many as 100 different attributes. However, only 5 to 20 of these variables typically have an impact on certain occurrences or conditions.

For example, income, education, and occupation may affect a customer’s likelihood to buy certain goods, but geography, gender, and family status may not. The need to pinpoint the most important variables, and what role they play in certain events, is prompting companies to adopt predictive modeling.

The ability to forecast the future, based on the past, can help companies be proactive instead of reactive to increase profits and reduce costs. Consider the case of inventory management. Organizations that accurately predict future demand can maximize revenues by having enough product on hand to satisfy all customers. At the same time, by precisely anticipating sales levels, they can save money and eliminate waste by avoiding stock surpluses.

Consider a credit card or loan company, which can mitigate risk and financial loss by using applicants’ histories to determine how likely they are to default on future payments.

What You Need in a Predictive Modeling Solution

Until recently, predictive modeling tools were standalone packages that delivered robust statistical analysis and data mining functionality but limited BI capabilities. These solutions were complicated to use, making them suitable only for researchers, statisticians, and other “experts.”

The true value of predictive modeling can only be achieved when it’s leveraged by operational users, who apply those same techniques to the information contained in databases and applications and use the outcomes to make immediate operational decisions. To make this possible, predictive modeling must be part of a broader solution that seamlessly unifies those features with data integration, reporting, and Web access. Only then will operational users be able to rapidly generate intuitive, easy-to-understand, forward-looking results through a user-friendly front end from information contained in any enterprise system.

This is particularly important when you consider that data preparation and manipulation accounts for approximately 60 to 90 percent of the cost of predictive modeling, and that, due to a lack of the right skills, most statistical research is never transformed into an application (i.e., it remains a research paper). By bringing these capabilities together, advanced statistical analysis and forecasting can be conducted in a far more cost-efficient manner and be used by many more people, even if they have no advanced statistical training.

Combating Cultural Resistance

Many companies experience push-back when introducing predictive modeling. Management is often the first group to raise objections, fearing that the results will be complicated and difficult to interpret, and therefore be unusable. Those creating the models must ensure that they are put in the proper form and business context, which will make them intuitive and highly relevant.

Operational users are another group that is often slow to adopt predictive modeling. They worry the result will be counterintuitive, uncovering mistakes in past decisions and proving their gut instinct wrong. It’s important to explain that predictive modeling will not be used to hold them accountable, or to doublecheck their choices, but to offer valid guidance when making those decisions.

Summary

By allowing everyone to more intelligently plan for the future, a predictive modeling environment—one that is embedded within a business intelligence infrastructure and widely adopted by operational workers—can empower companies to operate more efficiently and profitably than ever before.

This article originally appeared in the issue of .

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