Mapping and Comparing Data Governance Frameworks: A benchmarking exercise to inform global data governance deliberations

TL;DR

This paper benchmarks various data governance frameworks to understand their differences and similarities, emphasizing the need for a coordinated global approach to manage data responsibly across sectors.

Contribution

It provides a comparative analysis of data governance frameworks, highlighting patterns and gaps to inform the development of a more unified global data governance strategy.

Findings

Fragmented data governance practices across sectors

Identification of key elements in governance frameworks

Call for a holistic transnational data governance approach

Abstract

Data has become a critical resource for organizations and society. Yet, it is not always as valuable as it could be since there is no well-defined approach to managing and using it. This article explores the increasing importance of global data governance due to the rapid growth of data and the need for responsible data use and protection. While historically associated with private organizational governance, data governance has evolved to include governmental and institutional bodies. However, the lack of a global consensus and fragmentation in policies and practices pose challenges to the development of a common framework. The purpose of this report is to compare approaches and identify patterns in the emergent and fragmented data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the report highlights the need for a more holistic, coordinated transnational approach to data governance to manage the global flow of data responsibly and for the public interest. The article begins by giving an overview of the current fragmented data governance ecology, to then proceed to illustrate the methodology used. Subsequently, the paper illustrates the most relevant findings stemming from the research. These are organized according to six key elements: (a) purpose, (b) principles, (c) anchoring documents, (d) data description and lifecycle, (e) processes, and (f) practices. Finally, the article closes with a series of key takeaways and final reflections.