Measuring Master Data Quality

Data quality management (DQM) plays a critical role in all kinds of organizations (Pipino et al. 2002). With data being the foundation for information it is one of the most important criteria for strategic business decisions within organizations. That is why poor data quality can have a critical effect on business processes leading to increased costs and lowering customer and employee satisfaction (Redman 1998). Nearly every IT system has “dirty” (erroneous) data. About 75 per cent of organizations have identified costs originating from dirty data (Marsh 2005). U.S. businesses pay $600 billion a year due to a lack of data quality (Eckerson 2002). To improve data quality (DQ) as well as to evaluate the current status, the improvement of data quality and the effect of data quality initiatives have to be measured. Several authors point out that: “Only what can be measured can be improved” (Wand & Wang 1996, Wang & Strong 1996, English 1999). What is needed is a measurement approach to determine the level of data quality over time. Many companies are running data quality initiatives (White et al. 2006) focusing on actions to increase data quality. But how many of those really measure the quality of their data and how many really know how to implement a measurement system? The research presented in this paper was carried out in the context of the Competence Center Corporate Data Quality (CC CDQ), a research program on master data quality at the Institute of Information Management at the University of St. Gallen.