Organizational data mining : leveraging enterprise data resources for optimal performance

Title:

Personal Author:

Nemati, Hamid R., 1958-

Publication Information:

Hershey, Pa. : Idea Group Publishing, 2004

ISBN:

9781591401346

Subject Term:

Knowledge management

Data mining

Business -- Data processing

Added Author:

Barko, Christopher D., 1968-

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010075647	HD30.2 N45 2004	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	30000004987834	HD30.2 N45 2004	Open Access Book	Book	Searching... Unknown

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). This demonstrates how organizations can leverage ODM for enhanced competitiveness and optimal performance.

Author Notes

Hamid Nemati is an associate professor of information systems at the Information Systems and Operations Management Department of The University of North Carolina at Greensboro. He holds a doctorate from the University of Georgia and a Master of Business Administration from The University of Massachusetts. Before coming to UNCG, he was on the faculty of J. Mack Robinson College of Business Administration at Georgia State University. He has extensive professional experience in various consulting, business intelligence, and analyst positions and has consulted for a number of major organizations. His research specialization is in the areas of decision support systems, data warehousing, data mining, knowledge management and information privacy and security. He has presented numerous research and scholarly papers nationally and internationally. His articles have appeared in a number of premier professional and scholarly journals.

Hamid R. Nemati and Christopher D. BarkoHamid R. Nemati and Christopher D. BarkoRiad A. Ajami and Marca Marie Bear and Hanne NorreklitKate A. Smith and Mark S. DaleChandra S. Amaravadi and Farhad DaneshgarHamid R. Nemati and Charmion Brathwaite and Kara HarringtonRichard E. Potter and Pierre A. BalthazardStephen D. Durbin and Doug Warner and J. Neal Richter and Zuzana GHokey Min and Ahmed EmamWilliam L. TullarRahul Singh and Richard T. Redmond and Victoria YoonDavid M. Steiger and Natalie M. SteigerRustam VahidovCheryl Aasheim and Gary J. KoehlerHugh J. Watson and Barbara H. Wixom and Dale L. GoodhueYe-Sho Chen and Robert Justis and P. Pete ChongMary Jane Lenard and Pervaiz AlamScott Nicholson and Jeffrey StantonHenry Dillon and Beverley HopeJeff ZeanahIsabel Ramos and Joao Alvaro CarvalhoParviz Partow-Navid and Ludwig SluskyBernd Knobloch

Preface	p. viii
Section I Strategic Implications of ODM
Chapter I. Organizational Data Mining (ODM): An Introduction	p. 1
Chapter II. Multinational Corporate Sustainability: A Content Analysis Approach	p. 9
Chapter III. A Porter Framework for Understanding the Strategic Potential of Data Mining for the Australian Banking Industry	p. 25
Chapter IV. The Role of Data Mining in Organizational Cognition	p. 46
Chapter V. Privacy Implications of Organizational Data Mining	p. 61
Section II Business Process Innovations Througe ODM
Chapter VI. Knowledge Exchange in Organizations is a Potential, Not a Given: Methodologies for Assessment and Management of a Knowledge-Sharing Culture	p. 79
Chapter VII. Organic Knowledge Management for Web-Based Customer Service	p. 92
Chapter VIII. A Data Mining Approach to Formulating a Successful Purchasing Negotiation Strategy	p. 109
Chapter IX. Mining Meaning: Extracting Value from Virtual Discussions	p. 125
Section III ODM Analytics and Algorithms
Chapter X. An Intelligent Support System Integrating Data Mining and Online Analytical Processing	p. 141
Chapter XI. Knowledge Mining in DSS Model Analysis	p. 157
Chapter XII. Empowering Modern Managers: Towards an Agent-Based Decision Support System	p. 170
Chapter XIII. Mining Message Board Content on the World Wide Web for Organizational Information	p. 188
Section IV Industrial ODM Applications
Chapter XIV. Data Warehousing: The 3M Experience	p. 202
Chapter XV. Data Mining in Franchise Organizations	p. 217
Chapter XVI. The Use of Fuzzy Logic and Expert Reasoning for Knowledge Management and Discovery of Financial Reporting Fraud	p. 230
Chapter XVII. Gaining Strategic Advantage Through Bibliomining: Data Mining for Management Decisions in Corporate, Special, Digital, and Traditional Libraries	p. 247
Chapter XVIII. Translating Advances in Data Mining to Business Operations: The Art of Data Mining in Retailing	p. 263
Section V ODM Challenges and Opportunities
Chapter XIX. Impediments to Exploratory Data Mining Success	p. 280
Chapter XX. Towards Constructionist Organizational Data Mining (ODM): Changing the Focus from Technology to Social Construction of Knowledge	p. 300
Chapter XXI. E-Commerce and Data Mining: Integration Issues and Challenges	p. 321
Chapter XXII. A Framework for Organizational Data Analysis and Organizational Data Mining	p. 334
About the Authors	p. 357
Index	p. 367

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Table of Contents