A New Fuzzy Approach to Assess the Implementation of Data Governance and Management of Related Factors

Document Type : Original Article

Authors

1 Assistant Professor, Shahid Beheshti University.

2 Master Student, Shahid Beheshti University.

Abstract

Data in various organizations has become a valuable asset and data governance has become one of the priorities of the organization. A review of previous studies shows that the assessment of the implementation of the data governance system in organizations is done qualitatively and organizations cannot determine a plan to improve their situation based on this type of assessment. The purpose of this paper is to provide a quantitative way to measure the level of implementation of data governance in an organization and subsequently plan to improve the status quo. Due to the qualitative nature of the influential factors in measuring the success rate, fuzzy concepts have been used for modeling and analysis. Also, in order to consider how the factors affect each other, the fuzzy cognitive mapping technique has been used to model the causal relationships between the factors. Then, the identified influential factors have been weighted and prioritized using the best-worst fuzzy method and the DEMATEL technique, and subsequently, scenarios of improving the situation of the organization have been designed to plan for the effective allocation of limited resources of the organization. This research has significant practical implications for the organizations that intend to establish such a system or are in the middle stages of implementation and are looking for a solution to find a plan to improve the situation.

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Main Subjects


  1. Abraham, R., J. Schneider, & vom Brocke J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International Journal of Information Management, 49, 424-438.
  2. A hmadi, S., Yeh, C. H., Papageorgiou, E. I., & Martin, R. (2015). An FCM–FAHP approach for managing readiness-relevant activities for ERP implementation. Computers & Industrial Engineering88, 501-517.
  3. Alhassan, I., Sammon, D., & Daly, M. (2018). Data governance activities: A comparison between scientific and practice-oriented literature. Journal of Enterprise Information Management.
  4. Alhassan, I., Sammon, D., & Daly, M. (2019). Critical success factors for data governance: a theory building approach. Information Systems Management36(2), 98-110.
  5. Barker, J. M. (2016). Data Governance: the missing approach to improving data quality. University of Phoenix.
  6. Benitez-Amado, J., & Walczuch, R. M. (2012). Information technology, the organizational capability of proactive corporate environmental strategy and firm performance: a resource-based analysis. European Journal of Information Systems21(6), 664-679
  7. Brous, P., Janssen, M., & Vilminko-Heikkinen, R. (2016, September). Coordinating decision-making in data management activities: a systematic review of data governance principles. In International Conference on Electronic Government(pp. 115-125). Springer, Cham.
  8. Cheong, L. K., & Chang, V. (2007). The need for data governance: a case study. ACIS 2007 Proceedings, 100..
  9. CMMI-Institute. (2014). Data Management Maturity (DMM) Model
  10. Cupoli, P., Earley, S., & Henderson, D. (2014). Dama-dmbok2 framework. DAMA International.
  11. DalleMule, L., & Davenport, T. H. (2017). What’s your data strategy. Harvard Business Review95(3), 112-121.
  12. Data Governance Maturity Model. (2010). Available from: http://www.fstech.co.uk/fst/whitepapers/The_Data_Governance_Maturity_Model.pdf.
  13. Devaraj, S., Krajewski, L., & Wei, J. C. (2007). Impact of eBusiness technologies on operational performance: the role of production information integration in the supply chain. Journal of operations management25(6), 1199-1216.
  14. Even, A., Shankaranarayanan, G., & Berger, P. D. (2010). Managing the quality of marketing data: cost/benefit tradeoffs and optimal configuration. Journal of Interactive Marketing24(3), 209-221
  15. Fleckenstein, M., & Fellows, L. (2018). Data Governance. In Modern Data Strategy(pp. 63-76). Springer, Cham.
  16. Griffin, J. (2005). Data governance: A strategy for success. Information Management15(6), 49
  17. Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems121, 23-31
  18. Hushmandi Maher, M., M. Amiri, and L. Olfat. (2013). Integrated Supplier Selection Model in Supply Chain: An IT Capabilities Approach. Journal of Industrial Management Perspective, 2(4). 91-115. (In persian)
  19. IBM Data Governance. (2010). Available from: https://www.ibm.com/analytics/data-governance.
  20. Jafar Nejad Chaghushi, A., A. Kazemi, and A. Arab. (2016). Identify and prioritize supplier resilience assessment indicators based on the best-worst method. Journal of Industrial Management Perspective, 6(3), 159-186. (In persian)
  21. Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM53(1), 148-152.
  22. Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA journal42(4), 303-312.
  23. Nikshapoori, M., T. Abbasnejad, and R. Ahmadi Kahnali. (2019). Analysis of Causal Relationships between Green Productivity Indicators with Fuzzy Cognitive Mapping Approach. Journal of Industrial Management Perspective, 8(4), 97-119. (In persian)
  24. Oppenheim, C., Stenson, J., & Wilson, R. M. (2003). Studies on information as an asset I: Definitions. Journal of Information Science29(3), 159-166
  25. Otto, B. (2011). A morphology of the organisation of data governance
  26. Panian, Z. (2010). Some practical experiences in data governance. World Academy of Science, Engineering and Technology62(1), 939-946.
  27. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega53, 49-57
  28. Soares, S. (2015). Data governance tools: evaluation criteria, big data governance, and alignment with enterprise data management. Mc Press.
  29. Data Governance Maturity Model Guiding Questions for each Component-Dimension. (2011). Available from: http://web.stanford.edu/dept/pres-provost/cgi-bin/dg/wordpress/wpcontent/uploads/2011/11/StanfordDataGovernanceMaturityModel.pdf%E2%80%9D.
  30. Steinfield, C., Markus, M. L., & Wigand, R. T. (2011). Through a glass clearly: standards, architecture, and process transparency in global supply chains. Journal of Management Information Systems28(2), 75-108.
  31. Thomas, G. (2006). Alpha males and data disasters: the case for data governance. Brass Cannon Press.
  32. Weber, K., Otto, B., & Österle, H. (2009). One size does not fit all---a contingency approach to data governance. Journal of Data and Information Quality (JDIQ)1(1), 1-27
  33. Wies, R. (1994). Policies in network and systems management—Formal definition and architecture. Journal of Network and Systems Management2(1), 63-83