Steps to Success for the Mining Process

Steps to Success for the Mining Process

Stephan Kudyba (Economic Consultant, USA) and Richard Hoptroff (Consultant, The Netherlands)
Copyright: © 2001 |Pages: 15
DOI: 10.4018/978-1-930708-03-7.ch003
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Abstract

The previous chapters have given you some background on the core components of corporate IT systems along with software technology that promotes “business intelligence” throughout an enterprise. This included a good foundation on the high end analytical portion of information systems, namely data mining technology. All this sounds fantastic, state-of-the-art software that helps increase the flow of value-added information which leads to a reduction of uncertainty in a given business environment. However, the bottom line to the productivity enhancing process from IT implementation really entails proper management and utilization of this technology. In other words, an organization can spend huge sums of dollars on the best systems available, but if they are not implemented properly, their value and dollars invested become useless. Data mining technology is no exception. In fact, because of the more complex nature of the technology (e.g., statistics and mathematic underpinnings), the potential for underutilization or improper utilization is probably greater than other types of analytical applications. The following chapter provides some helpful hints on how to manage the mining process as it illustrates some common pitfalls that exist in conducting a high-end analysis. Remember, today’s technology is good, but it doesn’t do all the work for you.

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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)
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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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