Rethinking business intelligence

BI has a reputation for being a resource sink that delivers reports almost no one reads. It doesn’t have to be that way. And you can no longer afford to let it be

The commercial real estate firm General Investments and Development faced this dilemma, recalls CIO Shawn Mahoney. Different financial analysts had their own Excel formulas for calculating items such as internal rate of return, leading to inconsistent investment decisions. Rather than fight Excel, Mahoney implemented OutlookSoft, which uses Excel as a front end to an analysis engine and database, ensuring that everyone has the same data models and formulas for these decisions. “We got a standard process that everyone uses,” he says.

Weaving the BI fabric

The good news for IT is that it is easier to apply consistent BI technology to more operational systems, says Ovum’s Charlesworth, thanks to Web services, increased use of standards, more common APIs, and emerging concepts such as SOA. These newer approaches also help support consolidation of BI tools in the enterprise so that there can be a common analytics engine for typical processes such as finance and manufacturing.

Not only is it easier to use a common BI engine for many applications, “It’s easier to support a more dynamic approach to how we surface BI technology to users,” Charlesworth says. Remember Honerkamp’s goal of getting his IT group out of the query-and-reports business? He accomplished much of his objective by making his BI tool available to users via an enterprise portal. Rather than create queries and execute reports, developers at the Hillman Group created BI applications that can analyze specific business areas — and let business staff build their own queries on the fly using check boxes and pull-down menus.

That particular project actually helped the company improve the understanding of its own business. The first app IT created analyzed revenue — but in the definition phase, it became clear that the company had multiple ways of defining exactly what revenue was. “IT became the catalyst to get the groups together to agree upon a common definition of revenue before we would agree to build the app,” Honerkamp says. That not only eliminated a lot of data cleansing, it got the business on the same page about a fundamental financial issue for the first time.

Rather than filling endless requests for reports, Honerkamp’s team is now focused on working with economists and modelers to develop predictive modeling, a major shift in focus from plumbing history to preparing for future business activity. “The trick for us is to understand not just our lagging indicators but our leading indicators,” he says.

Going forward, enterprises should look for search and unstructured analytics tools that help make sense of text data and other information external to databases, says Ovum’s Davis. Such tools, most of which remain in the development phase, can augment BI’s quantitative analysis with qualitative analysis. A simple example: Call-center records can be analyzed for references to competitors to see, for example, which seem to be most attractive to new customers or which appear to be making good impressions on high-value customers, he says.

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Beyond structured data

Communications equipment maker Harris offers one example. The company has augmented its internal search capabilities with more traditional analytics, says Janice Lindsay, director of supply chain management. When engineers do a search for parts based on criteria such as power consumption or interface, an Endeca Technologies search engine looks at the raw results, then looks up quantitative information such as defect rates, available discounts, reliability ratings, and how much longer the part is expected to be manufactured. It then uses those factors to recommend which parts engineers should use. The results returned are filtered and ranked based on as many as 200 criteria, using information from ERP, manufacturing, product design, and other internal systems as well as from supplier systems and industry databases.

Through the use of dynamic summarization — a technique that does not require data cubes to be defined up front for the analysis tool to traverse — the Endeca Information Access Platform can analyze any data source for patterns, says Endeca’s Matt Eichner, vice president of strategic development.

Davis cites Factiva as another example of the unstructured analysis that might be brought to bear. The service searches Web sites and blogs to find mentions of companies, then analyzes the text to determine if the reference is favorable or not, ultimately producing a reputation index. Marrying this capability to traditional BI “is an interesting idea,” says Ovum colleague Charlesworth, “but it’s very early.” Davis estimates it to be about fives years out.

In the meantime, IT has plenty to do rationalizing its BI environment and meeting increased demand, and bringing BI into more operational aspects of the business. Remember one fundamental truth through it all, says Wachovia’s Thorpe: “You need to be business-driven, not IT-driven. Otherwise, you get a tool that no one uses.”

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