Building the Data Warehouse

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Problems with the Naturally
Evolving Architecture_

The naturally evolving architecture presents many challenges, such as:

■■ Data credibility ■■ Productivity

■ Inability to transform data into information

Lack of Data Credibility

The lack of data credibility is illustrated in Figure 1.3. Say two departments are delivering a report to management—one department claims that activity is down 15 percent, the other says that activity is up 10 percent. Not only are the two departments not in sync with each other, they are off by very large margins. In addition, trying to reconcile the departments is difficult. Unless very careful documentation has been done, reconciliation is, for all practical purposes, impossible.

Figure 1.3 Lack of data credibility in the naturally evolving architecture.

When management receives the conflicting reports, it is forced to make decisions based on politics and personalities because neither source is more or less credible. This is an example of the crisis of data credibility in the naturally evolving architecture.

This crisis is widespread and predictable. Why? As depicted in Figure 1.3, there are five reasons:

■■ No time basis of data

■■ The algorithmic differential of data

■■ The levels of extraction

■■ The problem of external data

■ No common source of data from the beginning

The first reason for the predictability of the crisis is that there is no time basis for the data. Figure 1.4 shows such a time discrepancy. One department has extracted its data for analysis on a Sunday evening, and the other department extracted on a Wednesday afternoon. Is there any reason to believe that analysis done on one sample of data taken on one day will be the same as the analysis for a sample of data taken on another day? Of course not! Data is always changing within the corporation. Any correlation between analyzed sets of data that are taken at different points in time is only coincidental.

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