Abstract
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.
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References
Delone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. J. MIS 19, 9–30 (2003)
DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3, 60–95 (1992)
English, L.P.: Information Quality Applied. Wiley, Indianapolis (2009)
Gartner: How to Overcome the Top Four Data Quality Practice Challenges. Gartner, Eghamm, UK (2017). https://www.gartner.com/doc/reprints?id=1-3W2JDZM&ct=170321&st=sb&elqTrackId=6d44157259df451482de51700cca6d36&elqaid=2250&elqat=2. Accessed 18 May 2017
Ge, M., Helfert, M.: Impact of information quality on supply chain decisions. J. Comput. Inf. Syst. 53(4), 59–67 (2013)
Ge, M., Helfert, M., Jannach, D.: Information quality assessment: validating measurement dimensions and process. In: Proceedings of 19th European Conference on Information Systems, Helsinki, Finland (2011)
Ge, M., Helfert, M.: Effects of information quality on inventory management. Int. J. Inf. Qual. 2(2), 176–191 (2008)
Gustafsson, P., Franke, U., Höök, D., Johnson, P.: Quantifying IT impacts on organizational structure and business value with extended influence diagrams. In: Stirna, J., Persson, A. (eds.) PoEM 2008. LNBIP, vol. 15, pp. 138–152. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89218-2_11
Haug, A., Zachariassen, F., van Liempd, D.: The cost of poor data quality. J. Ind. Eng. Manag. 4(2), 168–193 (2011)
Helfert, M., O’Brien, T.: Sustaining data quality – creating and sustaining data quality within diverse enterprise resource planning and information systems. In: Nüttgens, M., Gadatsch, A., Kautz, K., Schirmer, I., Blinn, N. (eds.) TDIT 2011. IAICT, vol. 366, pp. 317–324. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24148-2_25
Knight, S., Burn, J.: Developing a framework for assessing information quality on the World Wide Web. Inf. Sci. J. 8(5), 159–172 (2005)
Lukyanenko, R., Parsons, J.: Information quality research challenge: adapting information quality principles to user-generated content. ACM J. Data Inf. Qual. 6(1), 3 (2015)
Lee, H.L., Padmanabhan, V., Whang, S.: Information distortion in a supply chain: the bullwhip effect. Manag. Sci. 43(4), 546 (1997)
O’Brien, T., Sukumar, A., Helfert, M.: The value of good data- a quality perspective. In: International Conference of Enterprise Information Systems, Angers, France (2013)
Slack, N., Brandon-Jones, A., Johnston, R.: Operations Management, 8th edn. Pearson Education, Harlow (2016)
Wang, R.Y.: A product perspective on total data quality management. Commun. ACM 41(2), 58–65 (1998)
Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39(11), 86–95 (1996)
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Ge, M., O’Brien, T., Helfert, M. (2017). Predicting Data Quality Success - The Bullwhip Effect in Data Quality. In: Johansson, B., Møller, C., Chaudhuri, A., Sudzina, F. (eds) Perspectives in Business Informatics Research. BIR 2017. Lecture Notes in Business Information Processing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-64930-6_12
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DOI: https://doi.org/10.1007/978-3-319-64930-6_12
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