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What If: The Data Warehouse Becomes the Foundation for Collaboration

Reporting and analysis of performance, no matter how current, often delivers the user to the brink of decision-making. The user still needs to look into the future to assess options. As organizations emphasize collaboration throughout their trading community (B2B collaboration), it is becoming increasingly important to improve internal D2D (department to department) collaboration. The implication is that forward-looking assessment of decisions will be a shared responsibility. What's needed is "predictive intelligence," which supports forecasting performance metrics, "what if?" assessment, and user collaboration to reach consensus on the required action.

Introducing user collaboration to support a decision-making process identifies an overlooked feature of the data warehouse. One reason the data warehouse is expensive and time-consuming to maintain is that it enforces a language standard for reporting consistency. A language standard is required to support D2D and B2B collaboration.

What is a language standard? The data warehouse enforces a common vocabulary by defining products, services, markets, periods, facts, and calculated measures. The data warehouse also defines management hierarchical structures—for example, which sales territories are managed within each sales region. The language standard not only provides the common vocabulary, but it also creates consistency in reported measures and calculations. If collaboration is to be successful, there must be no ambiguity in what sales were last month or how marginal contribution is calculated.

Every analytic application that relies on the data warehouse as its information source inherits the language standard. This not only shortens the time to implement applications, but it also ensures that business changes are quickly reflected in every application.

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