Although managers consider accurate, timely, and relevant information as critical to the quality of their decisions, evidence of large variations in data quality abounds. Therefore, the question arises as to what factors influence an organisation to improve the quality of its data? Accordingly, the goal of this research was to develop and test a model of factors influencing the level of data quality within an organisation.
The model was tested using data collected from an in-depth case study at one organisation. The case study consisted of three components: a data quality survey, an action research project, and interviews with senior managers. The survey responses were used to test the research model and as the basis to formulate questions for follow-up interviews. The action research project attempted to investigate and track data quality initiatives undertaken by the participating organisation.
The action research project was conducted over a period of twelve months. The investigation focused on two types of errors: transaction input errors (accuracy) and processing errors (timeliness). Whenever the action research initiative identified non-trivial errors, the participating organisation introduced actions to correct the errors and prevent similar errors in the future. Data quality metrics were taken quarterly to measure improvements resulting from the activities undertaken during the action research project.
Seven senior managers were interviewed. The interview transcripts were analysed using the deductive analysis approach. The research model formed the basis of the deductive analysis framework. Constructs in the research model were used as pattern codes to identify issues. The interview responses were used to triangulate with the results of the survey and the action research project.
The survey results indicated that management commitment to data quality and the presence of data quality champions strongly influence data quality in the organisation. The action research project results indicated that for a mission-critical database, Vision, the error rates were moderately higher than the typical reports which range from one percent to 10 percent (Klein et al. 1997). To ensure and maintain data quality, the action research project found that commitment to continuous data quality improvement is necessary. Also, communication among all stakeholders is required to ensure common understanding of data quality improvement goals. The action research project found that to further substantially improve data quality, structural changes within the organisation and to the information systems are sometimes necessary.
Interview responses indicated the management of the participating organisation are committed to achieving and maintaining high data quality. Their responses also provided evidence supporting the research model. The interviews revealed that changing work processes and establishing data quality awareness culture are required to motivate further improvements to data quality.
A major goal of this study is to increase the level of data quality awareness in organisations and to motivate them to examine the importance of achieving and maintaining high-quality data. For organisations that have initiated data quality improvement programs, the results of the research could serve as a point of reference for comparison and improvement.
This research contributes to data quality research by presenting an in-depth study of how an organisation identified data quality issues and describes the actions it took to improve data quality. The action research project benefited the participating organisation in a number of ways. First, it helped the participating organisation to identify areas where poor quality data impacts on the business and to evaluate the materiality of those impacts. Second, it helped the participating organisation identify sources of poor data quality in its information systems. Third, it gave the organisation a quantitative assessment of the quality of the data in one of its most important information systems. Fourth, it provided insights for the participating organisation about how to further improve data quality. Fifth, it helped the participating organisation to prioritise data quality improvement actions and to establish strategic plans for data quality.