Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance

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.

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.

Hamid R. Nemati is an Associate Professor of Information Systems at the Information Systems and Operations Management Department of The University of North Carolina at Greensboro, USA. He holds a doctorate degree in Management Sciences and Information Technology from the University of Georgia and a Master of Business Administration from The University of Massachusetts. He has extensive professional IT experience as an analyst and has consulted with a number of major corporations. Before coming to UNCG, he was on the faculty of J. Mack Robinson College of Business Administration at Georgia State University.

His research specialization is in the areas of Organizational Data Mining, Decision Support Systems, Data Warehousing, and Knowledge Management. He has presented nationally and internationally on a wide range of topics relating to his research interests. His research has been published in a numerous top tier scholarly journals Christopher D. Barko is an information technology professional at Laboratory Corporation of America, USA. His IT industry experience spans many years in various consulting, business intelligence, software engineering and analyst positions for a number of Fortune 500 organizations. He received his B.B.A. in Computer Information Systems from James Madison University and M.B.A. from the University of North Carolina at Greensboro where he specialized in Decision Support Systems. His current research interests include Organizational Data Mining, Business Intelligence and Customer Relationship Management and how these technologies can enhance the organizational decision-making process to optimize resource allocation and improve profitability. His research has been published in several leading journals such as the Journal of Data Warehousing, Journal of Computer Information Systems, and others. He is also President of Customer Analytics, Inc., a consultancy that leverages advanced analytics to deliver profitable and effective database marketing solutions.