Building the Data Warehouse

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The first thing the DSS analyst discovers in trying to satisfy the request for information is that going to existing systems for the necessary data is the worst thing to do. The DSS analyst will have to deal with lots of unintegrated legacy applications. For example, a bank may have separate savings, loan, direct-deposit, and trust applications. However, trying to draw information from them on a regular basis is nearly impossible because the applications were never constructed with integration in mind, and they are no easier for the DSS analyst to decipher than they are for anyone else.

But integration is not the only difficulty the analyst meets in trying to satisfy an informational request. A second major obstacle is that there is not enough historical data stored in the applications to meet the needs of the DSS request.

Figure 1.8 shows that the loan department has up to two years’ worth of data. Passbook processing has up to one year of data. DDA applications have up to 60 days of data. And CD processing has up to 18 months of data. The applications were built to service the needs of current balance processing. They were never designed to hold the historical data needed for DSS analysis. It is no wonder, then, that going to existing systems for DSS analysis is a poor choice. But where else is there to go?

The systems found in the naturally evolving architecture are simply inadequate for supporting information needs. They lack integration and there is a discrep-

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