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A Value-Driven Model for Data Manufacturing: An Application for Optimal Error-Correction Policy

A Value-Driven Model for Data Manufacturing: An Application for Optimal Error-Correction Policy
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Author(s): Adir Even (Boston University School of Management, USA) and G. Shankaranarayanan (Boston University School of Management, USA)
Copyright: 2006
Pages: 4
Source title: Emerging Trends and Challenges in Information Technology Management
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59904-019-6.ch204
ISBN13: 9781616921286
EISBN13: 9781466665361


Data is an essential resource to the modern business. Corporate investments in data management solutions are increasing along with growing concerns regarding their cost and contribution to profitability. A data related factor identified as having a significant effect on cost and profitability is the quality of the organizational data resources [Redman, 1996]. The Data Quality Management (DQM) literature addresses the quality issue at different levels – from the high-level paradigm of Total Data Quality Management (TDQM) [Wang, 1998], to specific monitoring and improvement methodologies [e.g., Redman, 1996, Ballou et al., 1998]. Research, however, has not examined to the same extent, the economic contribution of DQM initiatives to business-value and profitability. The high costs of DQM initiatives make their contribution important to understand. To what extent do organizations gain value from data quality improvements? Does the value gained offset costs? How should considerations of value and cost affect DQM decisions? This study is a step towards better understanding the business value of DQM activities. It adopts the TDQM view of data management systems as a data manufacturing process (DMP) and their output as an information product (IP) [Wang, 1998, Ballou et al., 1998]. It introduces a value-driven framework for evaluation and optimization of DQM initiatives, based upon a DMP model that quantifies quality hazards and the value of associated managerial decisions. A new concept that this model introduces is the inclusion of business-value, cost, and profitmaximization considerations into process-optimization decisions. This allows assessing the overall cost of creating an IP, assessing the potential increase in IP profitability, and developing value-based optimal policies for managing quality. Section 2 introduces the DMP model that allows evaluation of decision alternatives, considering cost, value, and the accumulating stochastic effects. Section 3 demonstrates the use of the model in the development of a value-driven optimal error-correction policy. Section 4 offers conclusions and suggests directions for future research.

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