Building the Data Warehouse

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In most cases, the real benefits of the data warehouse are not known or even anticipated before construction begins because the warehouse is used differently than other data and systems built by information systems. Unlike most information processing, the data warehouse exists in a realm of “Give me what I say I want, then I can tell you what I really want.” The DSS analyst really cannot determine the possibilities and potentials of the data warehouse, nor how and why it will be used, until the first iteration of the data warehouse is available. The analyst operates in a mode of discovery, which cannot commence until the data warehouse is running in its first iteration. Only then can the DSS analyst start to unlock the potential of DSS processing.


For this reason, classical ROI techniques simply do not apply to the data warehouse environment. Fortunately, data warehouses are built incrementally. The first iteration can be done quickly and for a relatively small amount of money. Once the first portion of the data warehouse is built and populated, the analyst can start to explore the possibilities. It is at this point that the analyst can start to justify the development costs of the warehouse.


As a rule of thumb, the first iteration of the data warehouse should be small enough to be built and large enough to be meaningful. Therefore, the data warehouse is best built a small iteration at a time. There should be a direct feedback loop between the warehouse developer and the DSS analyst, in which they are constantly modifying the existing warehouse data and adding other data to the warehouse. And the first iteration should be done quickly. It is said that the initial data warehouse design is a success if it is 50 percent accurate.

Скачать в pdf «Building the Data Warehouse»