Beyond Paving The Cow Paths

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

Understand Cause and Effect

After identifying those factors that you'll use to scope your search, you need to understand why these drivers are critical to your housing decision. You need to understand the relationship between these driving factors — what makes them important — and the ultimate housing choice. You have now moved into the determine causal factors stage (stage 3). Here you refine your selection criteria, being more detailed in their definition and their corresponding acceptance criteria, such as:

  • School ranking in the top five in the city over the past year (because you have three school-age children)
  • Minimum of 3,200 square feet with four bedrooms and two bathrooms
  • One-half acre of a usable, mostly flat lot (room to play catch with the kids)
  • No more than a 30-minute drive to work (you don't want to spend more than five hours a week driving to work)
  • No more than a 20-minute drive to downtown shopping
  • In the price range of $350,000 to $400,000 (because you're not rich).

During Stage 3, the data warehouse designer focuses on understanding why these variables are important, how they interrelate with each other, and how they'll be used in making the final decision. The results of this phase typically result in even more detailed dimension tables, new data sources (typically third-party or nonelectronic causal data), and statistical routines to quantify the cause and effect of the relationships.

Evaluate the Options

After doing all the research and house tours, you can now create some sort of model to help you with the inevitable trade-offs in your final housing decision. You have now moved into the model alternatives stage (stage 4).

Models can be quite advanced statistical or spreadsheet algorithms or simple heuristics, rules of thumbs, or gut feeling. Whatever type of model used, its basic purpose is to provide a framework against which these different trade-off decisions can be evaluated. The model doesn't make the simple decision mundane, but helps make the seemingly impossible decision manageable.

You can employ your housing "model" to help you with the following types of housing trade-off decisions, perhaps using weighted averages in a spreadsheet to make the decision more quantitative vs. entirely qualitative:

  • Price of the house vs. the average neighboring prices
  • Price per square foot of the house vs. the neighborhood average
  • Price of the house vs. ranked quality of the school
  • Ranked quality of the school vs. number of minutes to work
  • Number of bedrooms vs. extra rooms (dens or sun rooms)
  • Square footage of the house vs. usability of the lot.

For the data warehouse designer, the analytics requirements gathering process focuses on the "model" that will be used in evaluating the different decision alternatives. This includes the metrics that will drive the ultimate decision (independent variables) and their relationships to the ultimate decision (dependent variable).

Track Actions for Future Optimization

And finally, once a decision has been made, you need to track the effectiveness of that decision in order to fine-tune the future decision process. That's the goal of the track actions stage (stage 5).

This stage is often skipped in the analytics process. Few people or organizations seem willing to spend the time to examine the effectiveness of their decisions. In our housing example, the same probably holds true. I'm not sure how many folks really consciously examine the effectiveness of their decision — until it comes time to sell their house. Then you quickly learn if the general marketplace values the factors that you valued.

  • Did I get the price appreciation that other neighborhoods got?
  • Was the quality of school what I thought it would be?
  • Did I have the access to work that I thought I would have?