Exploiting the Strategic Potential of Data Mining

Exploiting the Strategic Potential of Data Mining

Chandra S. Amaravadi (Western Illinois University, USA)
DOI: 10.4018/978-1-60566-026-4.ch237
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Abstract

Data mining is a new and exciting technology to emerge in the last decade. It uses techniques from artificial intelligence and statistics to detect interesting and useful patterns in historical data. Thus traditional techniques such as regression, Bayesian analysis, discriminant analysis, and clustering have been combined with newer techniques such as association, neural nets, machine learning, and classification (Jackson, 2002). Applications of data mining have ranged from predicting ingredient usage in fast food restaurants (Liu, Bhattacharyya, Sclove, Chen, & Lattyak, 2001) to predicting the length of stay for hospital patients (Hogl, Muller, Stoyan & Stuhlinger 2001). See Table 1 for other representative examples. Some of the important findings are: (1) corporate bankruptcies can be predicted from variables such as the “ratio of cash flow to total assets” and “return on assets,” (2) gas station transactions in the U.K. average £20 with a tendency for customers to round the purchase to the nearest £5 (Hand & Blunt, 2001), (3) 69% of dissatisfied airline customers did not contact the airline about their problem, (4) sales in fast food restaurants are seasonal and tend to peak during holidays and special events (Liu et al., 2001), (5) patients in the age group over 75 are 100% likely to exceed the standard upper limit for hospital stays (Hogl et al., 2001). In recent years, data mining has been extended to mine temporal, text, and video data as well. The study reported by Back, Toivonen, Vanharanta, and Visa (2001) analyzed the textual and quantitative content of annual reports and found that there is a difference between them and that poor organizational performance is often couched in positive terms such as “improving,” “strong demand,” and so forth. Both applications and algorithms are rapidly expanding. The technology is very promising for decision support in organizations. However, extracting knowledge from a warehouse is still considered somewhat of an art. This article is concerned with identifying issues relating to this problem.
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Introduction

Data mining is a new and exciting technology to emerge in the last decade. It uses techniques from artificial intelligence and statistics to detect interesting and useful patterns in historical data. Thus traditional techniques such as regression, Bayesian analysis, discriminant analysis, and clustering have been combined with newer techniques such as association, neural nets, machine learning, and classification (Jackson, 2002).

Applications of data mining have ranged from predicting ingredient usage in fast food restaurants (Liu, Bhattacharyya, Sclove, Chen, & Lattyak, 2001) to predicting the length of stay for hospital patients (Hogl, Muller, Stoyan & Stuhlinger 2001). See Table 1 for other representative examples. Some of the important findings are: (1) corporate bankruptcies can be predicted from variables such as the “ratio of cash flow to total assets” and “return on assets,” (2) gas station transactions in the U.K. average ₤20 with a tendency for customers to round the purchase to the nearest ₤5 (Hand & Blunt, 2001), (3) 69% of dissatisfied airline customers did not contact the airline about their problem, (4) sales in fast food restaurants are seasonal and tend to peak during holidays and special events (Liu et al., 2001), (5) patients in the age group over 75 are 100% likely to exceed the standard upper limit for hospital stays (Hogl et al., 2001). In recent years, data mining has been extended to mine temporal, text, and video data as well. The study reported by Back, Toivonen, Vanharanta, and Visa (2001) analyzed the textual and quantitative content of annual reports and found that there is a difference between them and that poor organizational performance is often couched in positive terms such as “improving,” “strong demand,” and so forth. Both applications and algorithms are rapidly expanding. The technology is very promising for decision support in organizations. However, extracting knowledge from a warehouse is still considered somewhat of an art. This article is concerned with identifying issues relating to this problem.

Table 1.
Examples of data mining applications
• Predicting supplies in fast food restaurants (Liu, Bhattacharyya, Sclove, Chen & Lattyak 2001).
• Quality of health care (Hogl, Muller, Stoyan & Stuhlinger 2001).
• Analyzing Franchisee sales (Chen, Justis & Chong 2003).
• Predicting customer loyalty (Ng & Liu 2001).
• Mining credit card data (Hand & Blunt 2001).
• Analyzing annual reports (Back et al. 2001).

Key Terms in this Chapter

Micro Theories: Beliefs that need to be tested during the data mining process.

Clustering: A technique in data mining that attempts to identify the natural groupings of data, such as income groups to which customers belong

Dimensionality: Dimensionality refers to the number of attributes. For instance, in sales data, “product,” “sales location,” and “time” are attributes

Overfitting: A condition that occurs when there are too many parameters in a model. In such cases, the model learns the idiosyncrasies of the test data set. This can happen in models such as regression, time series analysis, and neural networks

Classi fication: A technique in data mining that attempts to group data according to pre-specified categories such as “loyal customers” vs. “customers likely to switch.”

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