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December 5, 2002

The Promise of Decision Support

Analytic applications return to the original reason for the data warehouse

by Bill Schmarzo
Edited by Ralph Kimball

Many data warehouse implementations start by servicing the reporting needs of their business communities. They focus on providing a rearview mirror perspective on their business operations, but stop there and declare success. These projects fail to go beyond merely providing a bunch of prebuilt reports. Instead, they need to go further, to entwine analytic applications into the very fiber of an organization's decision-making processes.

When you study the most successful data warehouse approaches to business analysis, several common themes emerge: Decision makers in these organizations typically use exception-based analysis to identify opportunities, then dig deeper into the data to understand the causes of those opportunities. From there, they model the business situation (perhaps using a spreadsheet or statistical tool) so that they have a framework against which to evaluate different decision alternatives. Finally, they track the effectiveness of their decisions in order to continuously refine their decision-making capabilities.

Analytic Applications Life Cycle

The best of the new breed of analytic applications support this thoughtful, textured use of the data warehouse. A comprehensive analytic application environment needs to support a multistep framework that moves users beyond standard reports. The environment needs to proactively guide users through the analysis of the business situation, ultimately helping them make an insightful and thoughtful decision. The goals of this analytic application life cycle are to:

Guide business users beyond basic reporting
Identify and understand exceptional performance situations
Capture decision-making best practices for each exceptional performance situation
Share the resulting best practices or intellectual capital across the organization.

The analytic application life cycle comprises five distinct stages. (See Figure 1) The publish reports stage provides standard operational and managerial report cards on the current state of the business. The identify exceptions stage reveals exceptional performance (over- as well as underperformance) on which to focus attention. The determine causal factors stage seeks to understand the root causes behind the identified exceptions. The model alternatives stage aggregates what's been learned to model the business, providing a backdrop to evaluate different decision alternatives. The track actions stage analyzes the effectiveness of the recommended actions and feeds the decisions back to the operational systems and data warehouse (against which Stage 1 reporting will be conducted), thereby closing the loop.

Let's walk through each of these stages in a bit more detail to understand their objectives and the ramifications on the data warehouse architecture.

Publish reports. Standard operational and managerial reports are the necessary starting points for the five-step life cycle. These reports look at the current results vs. plan or previous periods in order to provide a report card on the state of the business. (For example, "Market share is up two points, but profitability is down 10 percent.")

Data warehouse requirements in the publish reports stage focus on improving the presentation layer and include presentation technologies such as dashboards, portals, and scorecards.