Building the Data Warehouse

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The local data warehouses often are housed on different technologies. In addition, the global data warehouse may be on a different technology than any of the local data warehouses. The corporate data model acts as the glue that holds the different local data warehouses together, as far as their intersection at the global data warehouse is concerned. There may be local data warehouses that house data unique to and of interest to the local operating site. There may also be a globally distributed data warehouse. The structure and content of the distributed global data warehouse are determined centrally, whereas the mapping of data into the global data warehouse is determined locally.

The coordination and administration of the distributed data warehouse environment is much more complex than that of the single-site data warehouse. Many issues relate to the transport of the data from the local environment to the global environment, including the following questions:

■    What network technology will be used?

■    Is the transport of data legal?

■    Is there a processing window large enough at the global site?

■    What technological conversion must be done?


Executive Information Systems and the Data Warehouse

Prior to data warehousing, there were executive information systems (EIS). EIS was a notion that computation should be available to everyone, not just the clerical community doing day-to-day transactions. EIS presented the executive with a set of appealing screens. The idea was that the elegance of the screen presentation would beguile the executive. While there certainly is merit to the idea that the world of computation should be open to the executive, the founders of EIS had no concept of the infrastructure needed to get those numbers to the executive. The entire idea behind EIS was presentation of information with no real understanding of the infrastructure needed to create that information in the first place. When the data warehouse first appeared, the EIS community roundly derided it as a complex discipline that required getting the hands dirty. EIS was a high-minded, elegant discipline that was above the hard work and management of complexity involved in a data warehouse. The EIS community decided that executives had better things to do than worry about such issues as sources of data, quality of data, currency of data, and so forth. And so EIS died for lack of an infrastructure. It hardly mattered that the presentation to the executive was elegant if the numbers being presented were unbelievable, inaccurate, or just plain unavailable.

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