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.
Beyond structured data
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.