Privacy Implications of Organizational Data Mining

Privacy Implications of Organizational Data Mining

Hamid R. Nemati (University of North Carolina at Greensboro, USA), Charmion Brathwaite (University of North Carolina at Greensboro, USA) and Kara Harrington (University of North Carolina at Greensboro, USA)
DOI: 10.4018/978-1-59904-937-3.ch171
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Abstract

Technological advances and decreased costs of implementing and using technology have allowed for vast amounts of data to be collected, used and manipulated for organizations to mine. If correctly deployed, Organizational Data Mining (ODM) offers companies an indispensable decision-enhancing process that optimizes resource allocation and exploits new opportunities by transforming data into valuable knowledge (Nemati & Barko, 2001). These tools have the potential to significantly reduce a company’s costs by helping to identify areas of potential business, areas that the company needs to focus its attention on or areas that should be discontinued because of poor sales or returns over a period of time. However, this information, if used in the wrong context, can be very harmful to an individual. As a result, ODM may “pose a threat to privacy” in the sense that discovered patterns can reveal confidential personal attributes about individuals. This paper examines a number of issues related to the privacy concerns that are inherent with the use of ODM.

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