Beyond Accuracy: What Data Quality Means to Data Consumers

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 Acknowledgments'. Research conducted herein has been supported, in part, by MIT's Total Data Quality Management (TDQM) Research Program and MIT's International Financial Services Research Center (IFSRC). The authors wish to thank Lisa Guarascio for her field work and factor analysis, Professors Donald Bailou, Izak Benbasat, and Stuart Madnick for providing insightful comments and encouragement, Professors France LeClerc and Paul Berger for their input on the design and execution of this research, and Daphne Png for conducting the confirmatory experiment and summarizing its results. Journal of Management Information Systems I Spring 1996, Vol. 12, No. 4, pp. 5-34 Copyright C 1996 M.E. Sharpe, Inc. This content downloaded from 128.95.130.223 on Sat, 26 Oct 2013 17:54:36 PM All use subject to JSTOR Terms and Conditions 6 RICHARD Y. WANG AND DIANE M. STRONG 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 of the 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.