Organizational Data Mining (ODM): An Introduction

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Author(s): Hamid R. Nemati (University of North Carolina at Greensboro, USA)and Christopher D. Barko (University of North Carolina at Greensboro, USA)
Copyright: 2004
Pages: 8
Source title: Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance
Source Author(s)/Editor(s): Hamid Nemati (The University of North Carolina at Greensboro, USA)and Christopher D. Barko (Laboratory Corporation of America, USA)
DOI: 10.4018/978-1-59140-134-6.ch001

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

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