Bringing It All Together (Data Mining on an Enterprise Level)

Bringing It All Together (Data Mining on an Enterprise Level)

Stephan Kudyba (Economic Consultant, USA) and Richard Hoptroff (Consultant, The Netherlands)
Copyright: © 2001 |Pages: 18
DOI: 10.4018/978-1-930708-03-7.ch008
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Abstract

Up to now we have presented the fundamental building blocks to understanding the concept of data mining and addressed the prevailing applications within the corporate environment including both the “brick and mortar” style and e-commerce spectrums. The process does not stop here however. In order to implement mining on an enterprise basis, firms must overcome some potentially serious obstacles and address key issues. The more complex nature of data mining generally limits its use to a smaller population of individuals in a given firm, (although this is not always the case). Because of this, a common drawback to the process of effective Mining is the communication of value-added model results to corresponding users of this information. Just as there exists a gap between IT personnel, (those who know the technical side of systems) and the business user, (those who require IT systems to help solve their problems), there also exists a communication gap between the “data miners” and those who need to apply the resulting models to help solve their business problem. Other issues which must be considered before implementing an organization wide mining approach entails the development of total mining solutions instead of limiting applications to a few business problems. Decision makers must also avoid the trap of relying too heavily on mining results and must remember that these models are not crystal ball providers of perfect knowledge. Because of this, they must therefore monitor actual business performance against projected measures to maintain model effectiveness and accuracy.

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