| Description |
1 online resource (xiii, 371 pages) : illustrations |
| Contents |
Section I. Strategic implications of ODM -- 1. Organizational Data Mining (ODM): An Introduction -- 2. Multinational Corporate Sustainability: A Content Analysis Approach -- 3. A Porter Framework for Understanding the Strategic Potential of Data Mining for the Australian Banking Industry -- 4. The Role of Data Mining in Organizational Cognition -- 5. Privacy Implications of Organizational Data Mining |
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Section II. Business process innovations through ODM -- 6. Knowledge Exchange in Organizations is a Potential, Not a Given: Methodologies for Assessment and Management of a Knowledge-Sharing Culture -- 7. Organic Knowledge Management for Web-Based Customer Service -- 8. A Data Mining Approach to Formulating a Successful Purchasing Negotiation Strategy -- 9. Mining Meaning: Extracting Value from Virtual Discussions |
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Section III. ODM Analytics and algorithms -- 10. An Intelligent Support System Integrating Data Mining and Online Analytical Processing -- 11. Knowledge Mining in DSS Model Analysis -- 12. Empowering Modern Managers: Towards an Agent-Based Decision Support System -- 13. Mining Message Board Content on the World Wide Web for Organizational Information |
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Section IV. Industrial ODM applications -- 14. Data Warehousing: The 3M Experience -- 15. Data Mining in Franchise Organizations -- 16. The Use of Fuzzy Logic and Expert Reasoning for Knowledge Management and Discovery of Financial Reporting Fraud -- 17. Gaining Strategic Advantage Through Bibliomining: Data Mining for Management Decisions in Corporate, Special, Digital, and Traditional Libraries -- 18. Translating Advances in Data Mining to Business Operations: The Art of Data Mining in Retailing |
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Section V. ODM Challenges and opportunities -- 19. Impediments to Exploratory Data Mining Success -- 20. Towards Constructionist Organizational Data Mining (ODM): Changing the Focus from Technology to Social Construction of Knowledge -- 21. E-Commerce and Data Mining: Integration Issues and Challenges -- 22. A Framework for Organizational Data Analysis and Organizational Data Mining -- About the Authors -- Index |
| Summary |
Mountains of business data are piling up in organizations every day. These organizations collect data from multiple sources, both internal and external. These sources include legacy systems, customer relationship management and enterprise resource planning applications, online and e-commerce systems, government organizations and business suppliers and partners. A recent study from the University of California at Berkeley found the amount of data organizations collect and store in enterprise databases doubles every year, and slightly more than half of this data will consist of "reference information," which is the kind of information strategic business applications and decision support systems demand (Kestelyn, 2002). Terabyte-sized (1,000 megabytes) databases are commonplace in organizations today, and this enormous growth will make petabyte-sized databases (1,000 terabytes) a reality within the next few years (Whiting, 2002). By 2004 the Gartner Group estimates worldwide data volumes will be 30 times those of 1999, which translates into more data having been produced in the last 30 years than during the previous 5,000 (Wurman, 1989) |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Print version record |
| Subject |
Knowledge management.
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Data mining.
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Business -- Data processing.
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Management -- Data processing.
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BUSINESS & ECONOMICS -- Information Management.
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BUSINESS & ECONOMICS -- Knowledge Capital.
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Data mining.
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Business -- Data processing.
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Knowledge management.
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Management -- Data processing
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Business -- Data processing
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Data mining
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Knowledge management
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| Form |
Electronic book
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| Author |
Barko, Christopher D., 1968-
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| LC no. |
2003008882 |
| ISBN |
1591401356 |
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9781591401353 |
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1591401348 |
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9781591401346 |
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1591402220 |
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9781591402220 |
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6610310475 |
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9786610310470 |
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1280310472 |
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9781280310478 |
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