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Many organizations have access to powerful analytic capabilities, but they often canít or donít apply any insights gained to work. They suffer from an "insight-to-action" gap that dampens smart decision- making from the BI and analytic tools in front of them. In fact, managers often don't really understand the role of analytics in the decisions they make.

Enter decision management systems, which not only are capable of analyzing the value of data to an organization, but then engage rules engines to automate the decision outcome. We're still early in the days of being able to automate key operational decisions, but this is clearly a compelling value proposition for businesses seeking rapid turnaround and consistency in their interactions.

What decision management technology is now available in the market? Are companies ready for automated decision management? To gain a perspective on these and related questions, we turned to Neil Raden and James Taylor, co-authors of "Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions" (Prentice Hall, 2007), for their insights. Raden, founder and CEO of Hired Brains, is considered a leading BI and analytics-industry influencer. Taylor, CEO of Decision Management Solutions, is an independent consultant on decision management, predictive analytics, business rules and related topics, and a former vice president at Fair Isaac Corp. The following Q & A includes highlights from our discussion.

Joe McKendrick: What kinds of solutions are considered to be part of the automated decisioning space: enterprise decision management, business rules engines, customer relationship management, business intelligence and analytics?

Neil Raden: There are two types of enterprise solutions: record-keeping and decision-making. Our business intelligence industry is just now coming to grips that informing people without aiding them in decision-making is like getting them to the Jordan River, but not across. In one form or another, every enterprise application has to have some form of decision management. In the past, rules were buried in source code. They were inflexible and brittle, could only be modified by some programmers and were poorly documented. By abstracting rules, de-coupling them from the applications, it is possible for subject matter experts to examine them, understand them and modify them.


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