Business rules, analytics meet at the forefront of decision management

Thanks to the still-lean economy, companies are seeking options for obtaining more precise, accurate and effective business decisions throughout their enterprises—and they’re looking to do so faster than ever before.

Not surprisingly, then, one of the hottest current trends in management is the increasing focus on real-time decision management. That right-now emphasis represents a shift away from processing decisions in batches or using decision management for less time-sensitive matters, according to James Taylor, CEO of Decision Management Solutions, a consulting firm in Palo Alto, Calif.

Businesses looking to adopt decision management are often seeking environments involving decisions made in fractions of a second, Taylor says. “There’s no realistic way for people to make such decisions manually.”

Another key decision management trend: A long-standing emphasis on risk and fraud prevention is gradually being replaced by a more customer-centric approach, focused on functions such as marketing, retention and helping guide customers to “the next best action,” Taylor says. “Automating customer decisions can be tied to customer-centricity and other, similar initiatives, linking the system to big corporate objectives,” he added.

Analytics is also becoming a big part of decision management initiatives. “Companies adopting decision management increasingly build dozens, hundreds, even thousands of predictive analytic models as part of their decision management initiatives,” Taylor says. “This is focusing them on industrializing their analytic processes and on effectively operationalizing their analytic models. Businesses should be aware that this is going to create a pull for more scalable analytic processes and tools.”

Just five years ago, most decision management implementations involved disconnected systems of analytics and business rules, and only a few vertical markets, such as financial services, were even interested, says Neil Raden, CEO and principal analyst at Hired Brains Inc., a consultancy based in Santa Fe, N.M.

“In last few years, analytics have gotten respectable,” he says. Most organizations are now thinking about how they can use predictive analytics and what they can do with “big data” and natural language processing to get feedback on how likely a particular customer is to buy.

Many vendors are tying together existing technologies such as business rules and predictive analytics to create decision management systems, says Mike Gualtieri, a principal analyst at Forrester Research in Cambridge, Mass. In fact, those two technologies complement each other especially well, playing to each other’s strengths and bridging gaps, he added.

As an example, Gualtieri cites customer turnover. Predictive analytics can examine information about a specific customer, determining whether that customer is likely to churn. Because predictive analytics can’t determine which action to take, business rules step in to offer options that customer service representatives might use to address the situation.

Another example involves a chain of retail stores. The chain’s headquarters might run a predictive model recommending that, for instance, stores could increase revenue by selling beer and cigarettes to minors, while business rules would prevent that from actually happening. “The predictive model can’t discriminate; [it just sees] market opportunity,” Gualtieri says. “Business rules can limit risk or implement company policies.”

But some of the more intricate analytics programs have raised questions about just how important business rules are, Raden says. Sentiment analysis and predictive modeling, among other technologies, let companies crunch data as well as determine what should be done with that data.

Such automated and semi-automated tools are moving toward the point where they can soon replace business rules, he says. While this kind of cognitive computing remains in its early stages, he still believes it can significantly reduce demand for business rules technology.

Vendors taking the lead to combine business rules, predictive analytics, sentiment analysis and other technologies will be at the forefront of decision management, Raden predicts.

“It’s not only that the software products are incompatible and require interfaces and have inherent disconnections between them, but you also have isolated groups separated by specialty and geography,” he says. “Once you’re able to weave these different things together, you can look at it as a single process. At that point, decision management, or decision processes, becomes a business process, instead of pieces.”

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Christine Parizo is a freelance writer specializing in business and technology. She's based in West Springfield, Mass. Contact her at

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