Building the Data Warehouse

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the claim information passes over to the data warehouse. As it does so, the claim information is summarized in several ways—by agent by month, by type of claim by month, and so on. At a lower level of detail, the claim is held in overflow storage for an unlimited amount of time. As in the other cases in which data passes to overflow, only data that might be needed in the future is kept (which is most of the information found in the operational environment).


Choosing the proper levels of granularity for the architected environment is vital to success. The normal way the levels of granularity are chosen is to use common sense, create a small part of the warehouse, and let the user access the data. Then listen very carefully to the user, take the feedback he or she gives, and adjust the levels of granularity appropriately.

The worst stance that can be taken is to design all the levels of granularity a priori, then build the data warehouse. Even in the best of circumstances, if 50 percent of the design is done correctly, the design is a good one. The nature of the data warehouse environment is such that the DSS analyst cannot envision what is really needed until he or she actually sees the reports.

The process of granularity design begins with a raw estimate of how large the warehouse will be on the one-year and the five-year horizon. Once the raw estimate is made, then the estimate tells the designer just how fine the granularity should be. In addition, the estimate tells whether overflow storage should be considered.

Скачать в pdf «Building the Data Warehouse»