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Business Intelligence Is Booming, Gartner Finds: Page 2

By Drew Robb     Feedback
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A recent update to the system has been the incorporation of unstructured data.  To carry out a project to analyze customer complaints received at contact centers, RBC initially planned to hire 20 more business intelligence specialists to conduct deep analysis. By bringing the data into the DWE platform, and bridging unstructured and structured information, the company achieved its objectives without having to hire the additional specialists.

The next big frontier, said Rifaie, is Big Data. The company has begun incorporating social media streams into the DWE picture. He gave an example of an ad campaign for a new home equity product. Immediate feedback showed that 16 percent of responses voiced concerns about hidden fees.

“By tracking social media, we were able to change our ad to emphasize that the new service was free,” he said. “The negative sentiment immediately vanished.”

Using Automation to Make Better Decisions

Making better decisions was a continuing theme throughout the conference.  While most organizations make a range of decisions, Sallam noted, there is a lack of consistency across decision makers and insight into how decisions are made. This inhibits effectiveness.

“Effective decision making at all levels of an organization separates high-performing companies from poor ones,” she said. “Decision making is so fundamental to success that improving it is the number one driver of BI and analytics.”

Despite all the money thrown at business intelligence, however, Sallam said the majority of organizations continue to lack a structure for standardizing the decision-making process. What is needed, she said, is to automate repeatable operational decisions in analytic applications to improve the quality and transparency of decisions. The process must include using the right data, analyzing only accurate data and applying it to the right problems in aligning with tactical and strategic priorities.

“Many decisions made by a line or operational worker are highly structured, repeatable and made at a high frequency with well-known decision logic,” Sallam said. “The degree of automation is high, and there is little human collaboration required apart from exception handling.”

She mentioned an emerging subcategory of analytics called intelligent decision automation (IDA), in which well-known decision rules and workflows are embedded in decision management tools such as rule engines. Obvious examples include the invoking of a rule engine to score an applicant's credit worthiness in a loan origination process, or deciding whether to authorize a loan.

“In many cases, the business policies are too complex or the possible variations too great to fully automate the decision,” Sallam said. “For such circumstances, an analytic or decision management service may still be embedded at a point of decision, but human intervention is required.”

An example: Once loan applications are approved or rejected, those falling in a gray approval area could be forwarded to a loan officer, along with supporting information to help him or her make a final ruling.

Drew Robb is a freelance writer specializing in technology and engineering. Currently living in California, he is originally from Scotland, where he received a degree in geology and geography from the University of Strathclyde. He is the author of Server Disk Management in a Windows Environment (CRC Press).

This article was originally published on April 6, 2012
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