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Beyond Accuracy: What Data Quality Means to Data Consumers

1996; Taylor & Francis; Volume: 12; Issue: 4 Linguagem: Inglês

10.1080/07421222.1996.11518099

1557-928X

Richard Y. Wang, Diane M. Strong,

Technology Adoption and User Behaviour

Abstract:Abstract:Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context ofthe task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers’ data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers’ experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.Key Words and Phrases: data administrationdata qualitydatabase systems Additional informationNotes on contributorsRichard Y. WangRichard Y. Wang is Associate Professor of Information Technologies (IT) and Co-Director for Total Data Quality Management (TDQM) at the MIT Sloan School of Management, where he received a Ph.D. degree with an IT concentration. He is a major proponent of data quality research, with more than twenty papers written to develop a set of concepts, models, and methods for this field. Professor Wang received more than one million dollars of research grants from both the public and private sector. His work on data quality was applied, by the Navy, to the Naval Command, Control, Communication, Computers, and Intelligence (C4I) information architecture. He presented the state-of-the-art of data quality research and practice in the Chief Information Officer (CIO) conference in 1993, and spoke on “Data Quality in the Information Highways” at the Enterprise ’93 conference. Dr. Wang organized the first Workshop on Information Technologies and Systems (WITS) in 1991. At WITS-94 he was elected chairman of the Executive Steering Committee. He also chaired or participated in the data quality panel at WITS and at the International Conference on Information Systems. Dr. Wang is the editor of Information Technologies: Trends and Perspectives.Diane M. StrongDiane M. Strong is Assistant Professor of Management at Worcester Polytechnic Institute. She received her Ph.D. in information systems from Carnegie Mellon University, an M.S. in computer science from the New Jersey Institute of Technology, and a B.S. in computer science from the University of South Dakota. Dr. Strong’s research centers on data and information quality and software quality. Her publications have appeared in ACM Transactions on Information Systems, MIS Quarterly, Decision Support Systems, and other leading journals.