Predicting Data Quality Success - The Bullwhip Effect in Data Quality

  • Mouzhi GeEmail author
  • Tony O’Brien
  • Markus Helfert
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 295)


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.


Data quality Bullwhip effect Data quality success Supply chain Data quality improvement 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic
  2. 2.Sheffield Hallam UniversitySheffieldUK
  3. 3.School of ComputingDublin City UniversityDublinIreland

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