Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a time of AI

• URL: http://arxiv.org/abs/2502.10399v1
• Date: Mon, 20 Jan 2025 16:24:22 GMT
• Title: Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a time of AI
• Authors: Stefaan Verhulst,
• Abstract summary: Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI)<n>Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application.<n>This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape.
• Score: 0.21756081703275998
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles. The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems. The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship.

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