Building the Data Warehouse

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In some cases, the lag of time can be much longer than 24 hours. If the data is not needed in the environment beyond the data warehouse, then it may make sense not to move the data into the data warehouse on a weekly, monthly, or even quarterly basis. Letting the data sit in the operational environment allows it to settle. If adjustments need to be made, then they can be made there with no impact on the data warehouse if the data has not already been moved to the warehouse environment.

The Feedback Loop

At the heart of success in the long-term development of the data warehouse is the feedback loop between the data architect and the DSS analyst, shown in Figure 9.4. Here the data warehouse is populated from existing systems. The DSS analyst uses the data warehouse as a basis for analysis. On finding new opportunities, the DSS analyst conveys those requirements to the data architect, who makes the appropriate adjustments. The data architect may add data, delete data, alter data, and so forth based on the recommendations of the end user who has touched the data warehouse.

data architect

Figure 9.4 The crucial feedback loop between DSS analyst and data architect.

A few observations about this feedback loop are of vital importance to the success of the data warehouse environment:

■■ The DSS analyst operates—quite legitimately—in a “give me what I want, then I can tell you what I really want” mode. Trying to get requirements from the DSS analyst before he or she knows what the possibilities are is an impossibility.

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