Building the Data Warehouse

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Though any of several subject areas may be candidates for the first global data warehouse development effort, many corporations begin with corporate finance. Finance is a good starting point for the following reasons:


■■ It is relatively stable.


■■ It enjoys high visibility.


■■ It is only a fraction of the business of the organization (except, of course, for organizations whose business is finance).


■    It is always at the nerve center of the organization.


■    It entails only a modicum of data.


In the case of the global warehouse being discussed, the Brazilian, the French, and the Hong Kong data warehouses would all participate in the building of a corporatewide financial data warehouse. There would be lots of other data in the operations of the Brazilian, French, and Hong Kong business units, but only the financial information would flow into the global data warehouse.


Because the global data warehouse does not fit the classical structure of a data warehouse as far as the levels of data are concerned, when building the global data warehouse, one must recognize that there will be some anomalies. One such anomaly is that the detailed data (or, at least, the source of the detailed data) resides at the local level, while the lightly summarized data resides at the centralized global level. For example, suppose that the headquarters of a company is in New York and it has outlying offices in Texas, California, and Illinois. The details of sales and finance are managed independently and at a detailed level in Texas, California, and Illinois. The data model is passed to the outlying regions, and the needed corporate data is translated into the form that is necessary to achieve integration across the corporation. Upon having made the translation at the local level, the data is transmitted to New York. The raw, untranslated detail still resides at the local level. Only the transformed, lightly summarized data is passed to headquarters. This is a variation on the theme of the classical data warehouse structure.

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