🤖 AI Summary
This study addresses the limitations of current data governance frameworks, which overly emphasize compliance and risk mitigation at the expense of enabling responsible cross-organizational data reuse for public benefit. To overcome this inward-looking paradigm, the paper proposes a novel institutional function—strategic data stewardship—centered on ecosystem collaboration and public value creation. It introduces an actionable Data Stewardship Canvas to operationalize this approach, integrating institutional design, governance principles, and practical mechanisms. The framework articulates core principles, roles, and capability models tailored to support real-world implementations in data collaboratives, data spaces, and data commons. By doing so, it aims to establish trustworthy, lawful, and efficient pathways for data reuse in the AI era, effectively bridging the gap between data availability and actual accessibility.
📝 Abstract
As societal challenges grow more complex, access to data for public interest use is paradoxically becoming more constrained. This emerging data winter is not simply a matter of scarcity, but of shrinking legitimate and trusted pathways for responsible data reuse. Concerns over misuse, regulatory uncertainty, and the competitive race to train AI systems have concentrated data access among a few actors while raising costs and inhibiting collaboration. Prevailing data governance models, focused on compliance, risk management, and internal control, are necessary but insufficient. They often result in data that is technically available yet practically inaccessible, legally shareable yet institutionally unusable, or socially illegitimate to deploy. This paper proposes strategic data stewardship as a complementary institutional function designed to systematically, sustainably, and responsibly activate data for public value. Unlike traditional stewardship, which tends to be inwardlooking, strategic data stewardship focuses on enabling cross sector reuse, reducing missed opportunities, and building durable, ecosystem-level collaboration. It outlines core principles, functions, and competencies, and introduces a practical Data Stewardship Canvas to support adoption across contexts such as data collaboratives, data spaces, and data commons. Strategic data stewardship, the paper argues, is essential in the age of AI: it translates governance principles into practice, builds trust across data ecosystems, and ensures that data are not only governed, but meaningfully mobilized to serve society.