Why the Term "Decision Support"?

Before I continue with my "Web Meets BI" learnings, let me explain why I prefer the term "Decision Support" to some of the other more fashionable terms like Business Intelligence and Performance Management.

I was introduced to Decision Support in 1984 when I worked for Metaphor Computer Systems. Metaphor developed and marketed a decision support software and hardware package that was used by Fortune 1000 companies to help them make better business decisions. And that last point ("make better business decisions") was made quite clear to me as I worked with companies such as Procter & Gamble, GD Searle, and Coors. These companies, as well as other companies, used the Metaphor system to make better business decisions. In my case, we used Metaphor to help our customers optimize marketing spend across brands and geographies, measure the effectiveness of promotional campaigns, forecast product line sales, identify trans-ship situations, make pricing decisions, and determine the financial viability of different acquisition candidates. Metaphor was truly used to help business users make better business decisions.

The Metaphor system was comprised of many important components, but the two most relevant for this discussion was the underlying data repository (a.k.a. data warehouse) and the end-user reporting and query tools (a.k.a. business intelligence). At the time, we didn't call these things "data warehouse" or "business intelligence." Those were just "capabilities" necessary to support decision making. Let me say that again: the "data warehouse" and the "business intelligence" components where there to support the decision making process. They were just components to a larger mission.

And I think that is still true today, despite what's been marketed over the past two decades. A data warehouse in of itself will not make better business decisions. A suite of business intelligence tools in of itself won't make better business decisions. But combine them together into an environment that is structured around a specific business challenge (e.g., increasing merchandising effectiveness, reducing inventory costs, increasing sales effectiveness) and with humans to help "interpret" the insights, then you have a winning decision support environment that can drive material business benefits to the organization.

My point here isn't to argue semantics, but is to point out that while data (structured in a format that is usable by humans) and tools (that are easy to use) are very important, they of themselves are not sufficient.

And that brings me back to the discussion of Decision Support 2.0.