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Developing a Method to Valuate the Collection of Big Data
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Author(s): Colleen Carraher Wolverton (University of Louisiana at Lafayette, Department of Management, Lafayette, USA), Brandi N. Guidry Hollier (University of Louisiana at Lafayette, Department of Management, Lafayette, USA), Michael W. Totaro (University of Louisiana at Lafayette, School of Computing and Informatics, Lafayette, USA)and Lise Anne D. Slatten (University of Louisiana at Lafayette, Department of Management, Lafayette, USA)
Copyright: 2019
Volume: 10
Issue: 1
Pages: 9
Source title:
International Journal of Strategic Decision Sciences (IJSDS)
Editor(s)-in-Chief: Saeed Tabar (Ball State University, USA)
DOI: 10.4018/IJSDS.2019010101
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Abstract
Although organizations recognize the potential of “big data,” implementation of data analytics processes can consume a considerable amount of resources. The authors propose that when organizations are considering this costly and often risky investment, they need a systematic method to evaluate the costs of data collection associated with the implementation of a new data and analytics (D & A) strategy or an expansion of an existing effort. Therefore, in this article, a new dimension of big data is proposed which is incorporated into a theoretically justified and systematic method for quantifying the costs and benefits of the data collection process. By estimating the worth of data, organizations can more efficiently focus on streamlining the collection of the most beneficial data and jettisoning less valuable data collection efforts.
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