Data Governance - A Comprehensive Overview
In today’s data-driven world, organisations face a critical challenge: how to extract value from data while ensuring security, compliance, and trustworthiness. Without a strong governance framework, even the most advanced AI and analytics initiatives risk failure due to poor data quality, misalignment, and regulatory concerns.
Our latest presentation and podcast explore the fundamentals, challenges, and evolving landscape of data governance, covering:
Defining Data Governance – Why there’s no single definition and how governance impacts people, processes, and technology.
Key Frameworks & Best Practices – A look at decision domains, structural mechanisms, and industry models like Khatri & Brown.
The Roadmap to Implementation – Practical steps to building an effective governance strategy.
Common Pitfalls & Challenges – From policy gaps to organizational silos, we break down the biggest obstacles.
Emerging Trends & The Future – How sustainability, AI impact assessments, and synthetic data are reshaping governance.
Watch, Listen & Learn
🎧 Listen to the podcast:
Why This Matters Now
As AI adoption accelerates, data governance is no longer optional—it’s essential for building scalable, ethical, and compliant systems. Whether you’re starting from scratch or refining your existing approach, this session provides the insights needed to navigate the evolving landscape.
How is your organization approaching data governance? Let’s start the conversation.
References
Atlan, T. (2024, September 28). Data Governance for AI: Challenges & Best Practices (2024). Atlan. https://atlan.com/know/data-governance/for-ai/
Bassi, C., & Alves-Souza, S. (2023). Challenges to Implementing Effective Data Governance: A Literature Review. In 15th International Conference on Knowledge Management and Information Systems (pp. 17–28). https://doi.org/10.5220/0012185900003598
Christoffersson, A., Hunt Karlsson, C., Department of Technology Management and Economics, Division of Quality Sciences, & Division of Logistics and Transportation. (2015). Developing a framework for Business Analytics: a structure for turning data into actionable insights. In Master’s thesis in Quality and Operations Management & Supply Chain Management (Report No E2015:092, Chalmers University of Technology). https://publications.lib.chalmers.se/records/fulltext/222200/222200.pdf
Chukwurah, N. N., Ige, N. a. B., Adebayo, N. V. I., & Eyieyien, N. O. G. (2024). Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries. Computer Science & IT Research Journal, 5(7), 1666–1679. https://doi.org/10.51594/csitrj.v5i7.1351
Kanying, T., Thammaboosadee, S., & Chuckpaiwong, R. (2023). Formulating Analytical Governance Frameworks: An Integration of Data and AI Governance Approaches. In IAIT ’23: Proceedings of the 13th International Conference on Advances in Information Technology (pp. 1–9). https://doi.org/10.1145/3628454.3628461
Kim, H. Y., & Cho, J. S. (2018). Data governance framework for big data implementation with NPS Case Analysis in Korea. Journal of Business & Retail Management Research, 12(03). https://doi.org/10.24052/jbrmr/v12is03/art-04
Machado Ribeiro, V. H., Barata, J., Rupino Da Cunha, P., & University of Coimbra, CISUC, DEI. (2022). Sustainable Data Governance: a systematic review and a conceptual framework. In 30TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS DEVELOPMENT (ISD2022 CLUJ -NAPOCA , ROMANIA) [Conference-proceeding]. https://estudogeral.uc.pt/bitstream/10316/115289/1/Sustainable%20Data%20Governance_%20A%20Systematic%20Review%20and%20a%20Conceptual.pdf
Smidt, N. (2021). Upon a Data Governance framework which uses data categorization (By Nico Brand, Jan-Martijn van der Werf, & Niels van den Broek). https://studenttheses.uu.nl/bitstream/handle/20.500.12932/483/Upon%20a%20data%20governance%20framework%20-%20Nick%20Smidt..pdf?sequence=1
Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Kirichenko, A., Marchal, S., Rodrigues, R., Santiago, N., Warso, Z., & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799–12831. https://doi.org/10.1007/s10462-023-10420-8
Walsh, M. J., McAvoy, J., & Sammon, D. (2022). Grounding data governance motivations: a review of the literature. Journal of Decision System, 31(sup1), 282–298. https://doi.org/10.1080/12460125.2022.2073637
