Beyond Paving the Cow PathsUse the five-stage analytic framework to deliver more from the data warehouseby Bill Schmarzo, Edited By Margy Ross Continued from Page 1 Understand Cause and EffectAfter 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:
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 OptionsAfter 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:
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 OptimizationAnd 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.
For the data warehouse designer, the analytics requirements gathering process needs to capture the decision or actions taken, ideally in the data warehouse. With this information captured, the business user can see if an action had the desired impact upon the key driving business metrics (such as revenue, share, profitability, or customer satisfaction). As you can see, reporting is typically the starting point for the analysis, but it isn't the end-state goal. Only when an organization is able to move beyond just the reporting do you start to see the business return associated with making better decisions. Guest columnist Bill Schmarzo [schmarzo@decisionworks.com] has two decades of data warehousing, customer relationship management, and analytic applications experience.
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