Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run

Sustaining Data Quality – Creating and Sustaining Data Quality within Diverse Enterprise Resource Planning and Information Systems

Part 6: Research in Progress and PracticeInternational audienceMany studies have confirmed the challenges relating to data quality in enterprises. This practice oriented research confirms the premise that data quality is of paramount importance to the efficiency and effectiveness of all organizations and that data quality management needs to be embedded within the organizational routines, practices and processes. In this paper we present a study on how to incorporate data quality management principles into organisations. The overriding measure for ‘real’ success is the sustainability of quality data, thus improving the quality of data over time, to engender long term success. The proposed principles and concepts were applied within a case study. The conclusions drawn from this study contends that this research has unearthed new knowledge as to the means by which data quality improvements may be sustained within diverse enterprise planning and information systems