Master Data Domains Prioritization and Privacy Classification in Government Agencies: An empirical study of Malaysia Local Government


  • Faizura Haneem
  • Nazri Kama
  • Azri Azmi
  • Azizul Azizan
  • Suriani Mohd Sam





data management, master data, master data management (MDM), government agencies


Master data is the most valuable information in each government agency such as customers, suppliers, products, accounts, and the relationships between them. In government agencies, master data are scattered across various agencies, managed in a silo environment in multiple applications and database systems. This silo data management issue led to data quality issues such as data duplication and also may cause disaster to the agencies due to the complexity and higher cost and resource requirement. To resolve the issue, the Master Data Management (MDM) implementation at central level could minimize the data duplication problem by establishing a single source of truth by consolidating and integrating multiple master data from multiple agencies into central repository. The central repository can be referred by other applications across multiple agencies instead of creating new entities in their local database systems. However, in the initial stage of the MDM implementation process, master data across government agencies must be assessed and prioritized to ensure the success of the implementation. Hence, this study aims to clarify the domains prioritization and the privacy classification of master data in government agencies by using a qualitative and quantitative data analysis approach. It involves participative case studies from seven (7) Malaysia’s local government agencies. The study identifies 36 sets of master data which generally prioritized into three domains which are; 1) services and products, 2) customers, and 3) service providers. From these master datasets, 20 datasets (56%) are classified as open data. The result of this study is in contrast with most of the current literatures that stated the MDM typically prioritize on the customer data domain as compared to other domains. This study also indicates that the government agency has a high potential to share these open master data to the centralized MDM platform with worry-free of the privacy issues.



[1] Allen, M., & Delton Cervo. (2015). Multi Domain Master Data Management. Retrieved from

[2] Bonnet, P. (2013). Enterprise Data Governance. Enterprise Data Governance: Reference & Master Data Management, Semantic Modeling. Hoboken, NJ, USA: John Wiley & Sons, Inc.

[3] Cervo, D., & Allen, M. (2011). Master Data Management in Practice: Achieving True Customer MDM. New Jersey: John Wiley & Sons.

[4] Clifton, C., Kantarcioǧlu, M., Doan, A., Schadow, G., Vaidya, J., Elmagarmid, A., & Suciu, D. (2004). Privacy-preserving data integration and sharing. Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery - DMKD ’04, 19.

[5] Creswell, J. W. (2014). Research design: qualitative, quantitative and mixed methods approaches.

[6] Dreibelbis, A. (2008). Enterprise Master Data Management: An SOA Approach to Managing Core Information. Retrieved from

[7] Gartner. (2003). SAP’s MDM Shows Potential, but Is Rated “Caution.â€

[8] Linstone, H. a, & Turoff, M. (2002). The Delphi Method - Techniques and applications. The Delphi Method - Techniques and Applications, 1–616.

[9] Loshin, D. (2009). Master Data Management. Master Data Management, 43–65.

[10] Merriam-Webster Dictionary. (2011). Retrieved February 24, 2016, from

[11] Otto, B., Hüner, K. M., & Österle, H. (2012). Toward a functional reference model for master data quality management. Information Systems and E-Business Management, 10(3), 395–425.

[12] Ranjit Kumar. (2011). Research Methodology - a step-by-step guide for beginners (3rd Editio). SAGE.Services, P. (2008). End-to-End Efficiency : Public Services, (July 2004).

[13] Silvola, R., Jaaskelainen, O., Kropsu-Vehkapera, H., & Haapasalo, H. (2011). Managing one master data – challenges and preconditions. Industrial Management & Data Systems, 111(1), 146–162.

[14] Smith, H. A., & McKeen, J. D. (2008). Developments in Practice XXX : Master Data Management : Salvation Or Snake Oil ? Master Data Management : Salvation Or Snake Oil ? Communications of the Association for Information Systems, 23(1), 4.

[15] Spruit, M., & Pietzka, K. (2014). MD3M: The master data management maturity model. Computers in Human Behavior.

[16] Thenint, H., & Miles, I. (n.d.). Innovation Policy Challenges for the 21st Century. (D. Cox & J. Rigby, Eds.). Routledge. Retrieved from

[17] W3C(e-Gov). (2009). eGovernment at W3C: improving access to government through better use of the web. Retrieved from

View Full Article: