Why does BI miss the mark often ?

Most corporations with major Business Intelligence (BI) deployments create large data warehouses or collections of data marts whose incoming data must be cleansed to ensure integrity and consistency — and whose relationships must be clearly defined in data cubes to ensure that analysis tools can run queries embedded in standard reports.

But if you start with that sort of architectural model, you’re likely to fail, says Scott Sognefest, a partner in Deloitte Consulting’s BI practice. “There’s a growing realization that you can’t put BI technology on top of a big pile of data. It’s expensive and inefficient,” he says. “You wouldn’t build a factory and then decide what products you want to produce after it’s built, but that’s what people do in the BI space.”

It’s an elusive goal one seeks between complex IT requirements that are expensive and practical user empowerment that’s fairly elementary. Interesting article from Infoworld here.

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