Beyond Accuracy: What Data Quality Means to Data Consumers
- 1 March 1996
- journal article
- Published by Taylor & Francis Ltd in Journal of Management Information Systems
- Vol. 12 (4), 5-33
- https://doi.org/10.1080/07421222.1996.11518099
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.Keywords
This publication has 22 references indexed in Scilit:
- Designing Information Systems to Optimize the Accuracy-Timeliness TradeoffInformation Systems Research, 1995
- The Voice of the CustomerMarketing Science, 1993
- Information Systems Success: The Quest for the Dependent VariableInformation Systems Research, 1992
- Data qualityInformation and Software Technology, 1990
- Methodology for allocating resources for data quality enhancementCommunications of the ACM, 1989
- Data quality and due process in large interorganizational record systemsCommunications of the ACM, 1986
- Modeling Data and Process Quality in Multi-Input, Multi-Output Information SystemsManagement Science, 1985
- The dimensions of accessibility to online information: implications for implementing office information systemsACM Transactions on Information Systems, 1984
- The measurement of user information satisfactionCommunications of the ACM, 1983
- Development of a Tool for Measuring and Analyzing Computer User SatisfactionManagement Science, 1983