An analyst derives only a fraction of the knowledge that is potential if he fails to use the correct analytical methodology. Analysts can therefore generate considerable loss in value, if they are the weak link in the process.

In Chapter 7 we will also discuss how to set up processes that make the analyst more efficient. For example, we once were given the task of developing an analytical factory for a large telecom provider. Our work reduced the average time it took to develop an analytical model from approximately two months to less than six hours (organizational sign-off included).

Analytical Methods (Information Domains)

In the previous section, we discussed the analyst's role in the overall BA value chain, which stretches from collecting data in the technical part of the organization to delivering information or knowledge to the business-oriented part of the organization. We outlined some requirements of the analytical function, one of which was that it must function as a bridge between the technical side and the business side of the organization and thereby form a value chain or a value-creating process.

Another requirement is that the analytical function must possess methodical competencies to prevent loss of information. Loss of information occurs when the accessible data in a data warehouse, provided it is retrieved and analyzed in an optimum way, has the potential of delivering business support of a certain quality, but cannot because this quality is compromised. Reasons for this lack might be the simple failure to collect the right information, which might, in turn, be due to lack of knowledge about the data or lack of understanding of how to retrieve it.

But errors might also be traced to the analyst not having the necessary tool kit in terms of methodology. When this is the case, the analyst derives only a fraction of the knowledge that is potentially there. If we therefore imagine that we have a number of analysts who are able to extract only 50 percent of the potential knowledge in the data warehouse in terms of business requirements, we have a corresponding loss from our data warehouse investment. When we made the decision to invest in a data warehouse based on our business case, we naturally assumed that we would obtain something close to the maximum knowledge. Instead, we end up getting only half the return on our investment. That means that the data warehouse investment in the business case should have been twice as big. If we look at the business case from this perspective, it might not have been a profitable decision to acquire a data warehouse, which means the investment should not have been made. Analysts can therefore generate considerable loss in value if they are the weak link in the process.

Therefore, in the following section we have prepared a list of methods that provide the BA department with a general knowledge of the methodological spectrum, as well as a guide to finding ways around it.

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