Building the Data Warehouse

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The second problem with summary data is that it may or may not be at the appropriate level of granularity for the analytical purpose at hand. A balance needs to be struck between the level of detail and the level of summarization for EIS and DSS processing.


There is a very strong affinity between the needs of the EIS analyst and the data warehouse. The data warehouse explicitly supports all of the EIS analyst’s needs. With a data warehouse in place, the EIS analyst can be in a proactive rather than a reactive position.

The data warehouse enables the EIS analyst to deal with the following management needs:

■■ Accessing information quickly ■■ Changing their minds (i.e., flexibility)

■■ Looking at integrated data ■■ Analyzing data over a spectrum of time ■ Drilling down

The data warehouse provides an infrastructure on which the EIS analyst can build.


External/Unstructured Data and the Data Warehouse

Most organizations build their first data warehouse efforts on data whose source is existing systems (i.e., on data internal to the corporation). In almost every case, this data can be termed internal, structured data. The data comes internally from the corporation and has been already shaped into a regularly occurring format.

A whole host of other data is of legitimate use to a corporation that is not generated from the corporation’s own systems. This class of data is called external data and usually enters the corporation in an unstructured, unpredictable format. Figure 8.1 shows external and unstructured data entering the data warehouse.

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