Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance

Przednia okładka
Hamid R. Nemati, Christopher D. Barko
Idea Group Inc (IGI), 1 sty 2004 - 371
Successfully competing in the new global economy requires immediate decision capability. This immediate decision capability requires quick analysis of both timely and relevant data. To support this analysis, organizations are piling up mountains of business data in their databases every day. 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). Those organizations making swift, fact-based decisions by optimally leveraging their data resources will outperform those organizations that do not. A technology that facilitates this process of optimal decision-making is known as Organizational Data Mining (ODM). Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance demonstrates how organizations can leverage ODM for enhanced competitiveness and optimal performance.
 

Spis treści

A Content Analysis Approach
9
A Porter Framework for Understanding the Strategic Potential of Data
25
Privacy Implications of Organizational Data Mining
61
Organic Knowledge Management for WebBased Customer Service
92
A Data Mining Approach to Formulating a Successful Purchasing
109
Extracting Value from Virtual Discussions
125
ODM ANALYTICS AND ALGORITHMS
140
Knowledge Mining in DSS Model Analysis
157
INDUSTRIAL ODM APPLICATIONS
201
Data Mining in Franchise Organization
217
The Use of Fuzzy Logic and Expert Reasoning for Knowledge
230
Data Mining
247
ODM CHALLENGES AND OPPORTUNITIES
279
Integration Issues and Challenges
321
A Framework for Organizational Data Analysis and Organizational
334
About the Authors
357

Towards an AgentBased Decision
170
Mining Message Board Content on the World Wide Web
188

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Strona 355 - Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp.

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