Building the Data Warehouse

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Figure 2.10 shows the typical process of building a data warehouse. On day 1 there is a polyglot of legacy systems essentially doing operational, transactional processing. On day 2, the first few tables of the first subject area of the data warehouse are populated. At this point, a certain amount of curiosity is raised, and the users start to discover data warehouses and analytical processing.

On day 3, more of the data warehouse is populated, and with the population of more data comes more users. Once users find there is an integrated source of data that is easy to get to and has a historical basis designed for looking at data over time, there is more than curiosity. At about this time, the serious DSS analyst becomes attracted to the data warehouse.

On day 4, as more of the warehouse becomes populated, some of the data that had resided in the operational environment becomes properly placed in the data warehouse. And the data warehouse is now discovered as a source for doing analytical processing. All sorts of DSS applications spring up. Indeed, so many users and so many requests for processing, coupled with a rather large volume of data that now resides in the warehouse, appear that some users are put off by the effort required to get to the data warehouse. The competition to get at the warehouse becomes an obstacle to its usage.

On day 5, departmental databases (data mart or OLAP) start to blossom. Departments find that it is cheaper and easier to get their processing done by bringing data from the data warehouse into their own departmental processing environment. As data goes to the departmental level, a few DSS analysts are attracted.

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