Beyond Paving The Cow Paths - InformationWeek

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2/19/2004
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Beyond Paving The Cow Paths

Use the five-stage analytic framework to deliver more from the data warehouse.

This article was originally published in November 2003.

Some data warehouse designers want to declare warehouse victory after merely replicating the organization's top five reports. They're satisfied with this level of deliverable because "that's what the users asked for." However, this approach is akin to paving the cow paths. In some communities, the roadways resemble a tangled web because early roads were built on preexisting cow paths. Unfortunately, the cows didn't meander along straight grid lines. Similarly, merely using the data warehouse to pave reporting "cow paths" doesn't push the organization beyond what it has today. This is where the analytic life cycle can help.

In "The Promise of Decision Support" (Dec. 5, 2002), we introduced the five-stage analytic life cycle.

Stage 1: Publish reports supports standard operational and managerial reporting on the current state of the business.

Stage 2: Identify exceptions pinpoints unusual performance situations that warrant further attention.

Stage 3: Determine causal factors seeks to understand the causal factors behind the exceptions.

Stage 4: Model alternatives synthesizes what's been learned to build a model for evaluating alternatives and trade-offs.

Stage 5: Track actions analyzes the effectiveness of the recommended actions and feeds the results back to the operational and data warehouse systems. We then return back to Stage 1 to report on these results, thereby closing the loop.

To move beyond replicating reports, you can use the analytic life cycle for gathering more in-depth business requirements. It provides a framework to collaborate with users to understand their analytic processes. It forces data warehouse designers to ask the second- and third-level questions, the "hows" and "whys," to understand how the organization could leverage the data warehouse for analysis.

Begin with Reported Results

Most analyses start with a report, which details business performance metrics. Our challenge is to push beyond into the more detailed analytic requirements.

Let's walk through a real-world experience — buying a house — in order to understand how the analytics life cycle guides the analytics requirements gathering process. Let's say that you've been transferred to a new city, and you have to find a new house. What sort of process do you use to find that ideal house? You might start with a couple of real estate listings (and the guidance of a knowledgeable real estate agent) and begin asking a lot of questions:

  • What neighborhoods have the best schools?
  • What neighborhoods are closest to my job?
  • What can I afford?

For the data warehouse designer, reporting requirements are the starting point. You need to take the time to identify and understand which reports the business relies on to monitor their performance. However, users can't possibly look at all the data. You need to take the analysis process to the next level.

Identify Criteria and Threshold Tolerances

When house hunting, you need to limit your search; otherwise you'll be inundated by all the housing options (especially considering that houses are constantly moving on and off the market). You can reduce the number of housing options by identifying only those properties that meet a certain set of criteria. You've now moved into the identify exceptions stage (stage 2). In the housing example, these critical criteria might include:

  • Price range
  • Quality of schools
  • Safety of neighborhood
  • Square footage of the house.

Stage 2 guides the data warehouse designer to look for requirements that focus on identifying the factors and thresholds that identify unusual situations worthy of further analysis. The exception identification factors typically manifest themselves as new facts and dimension attributes.

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