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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Table of Contents
Acknowledgments
Chapter 1
Stephan Kudyba, Richard Hoptroff
The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information... Sample PDF
An Introduction to Information Technology and Business Intelligence
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Chapter 2
Data Mining Defined  (pages 23-42)
Stephan Kudyba, Richard Hoptroff
Over the years, the term data mining has been connected to various types of analytical approaches. In fact, just a few years ago, let’s say prior to... Sample PDF
Data Mining Defined
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Chapter 3
Stephan Kudyba, Richard Hoptroff
The previous chapters have given you some background on the core components of corporate IT systems along with software technology that promotes... Sample PDF
Steps to Success for the Mining Process
$37.50
Chapter 4
Stephan Kudyba, Richard Hoptroff
Forecasting and “what if” mining generally incorporates the application of regression and neural network methodologies. In certain cases, for more... Sample PDF
Essential Mining Approaches to Problem Solving
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Chapter 5
Stephan Kudyba, Richard Hoptroff
Two core business strategies throughout the realm of commerce, which take their root from traditional economic theory, involve the incorporation of... Sample PDF
A Closer Look at Marketing/Advertising, Promotions and Pricing Policies Using Econometric Based Modeling
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Chapter 6
Stephan Kudyba, Richard Hoptroff
I recently read an article about words and terms that evolved exclusively from the American culture. This piece traced the history of American-born... Sample PDF
Turning Your Brick and Mortar into a Click and Mortar
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Chapter 7
Stephan Kudyba, Richard Hoptroff
The previous chapter introduced the evolution of the information economy as it addressed the progress of commerce from “brick and mortar” to “click... Sample PDF
Improving the Web Experience through Real-Time Analysis (A Market Basket Approach)
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Chapter 8
Stephan Kudyba, Richard Hoptroff
Up to now we have presented the fundamental building blocks to understanding the concept of data mining and addressed the prevailing applications... Sample PDF
Bringing It All Together (Data Mining on an Enterprise Level)
$37.50
Chapter 9
Stephan Kudyba, Richard Hoptroff
Predicting the future is always a difficult task, of course that depends on how far into the future one attempts to delve. With regard to data... Sample PDF
What the Future Holds for Data Mining
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