Building the Data Warehouse

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■■ Creating special extract programs from existing systems

■■ Dealing with unintegrated data

■■ Compiling and linking detailed and summary data and the linkage between the two

■    Finding an appropriate time basis of data (i.e., does not have to worry about finding historical data)

■    Management constantly changing its mind about what needs to be looked at next

In addition, the EIS analyst has a rich supply of summary data available.

day 1

Management is interested in financial activities.

day 2

There is a production problem.



Suddenly there is a    financial

shipment problem.

Figure 7.7 The constantly changing interests of executives.


data warehouse

In short, the data warehouse provides the basis of data-the infrastructure—that the EIS analyst needs to support EIS processing effectively. With a fully populated data warehouse in place, the EIS analyst can be in a proactive stance—not an eternally reactive stance—with regard to answering management’s needs. The EIS analyst’s job changes from that of playing data engineer to that of doing true analysis, thanks to the data warehouse.

Yet another very important reason why the data warehouse serves the needs of the world of EIS is this: The data warehouse operates at a low level of granularity. The data warehouse contains—for lack of a better word—atomic data. The atomic data can be shaped one way, then another. When management has a new set of needs for information that has never before been encountered in the corporation, the very detailed data found in the data warehouse sits, waiting to be shaped in a manner suited to management’s needs. Because of the granular atomic data that resides in the data warehouse, analysis is flexible and responsive. The detailed data in the data warehouse sits and waits for future unknown needs for information. This is why the data warehouse turns an organization from a reactive stance to a proactive stance.

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