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

TL;DR
This paper clarifies the diverse forms of data stewardship, emphasizing its role in AI, and discusses core competencies, principles, and challenges to enhance data governance and collaboration.
Contribution
It introduces four manifestations of data stewardship, distinguishes roles like stewards and CDOs, and highlights emerging principles such as AI readiness in data governance.
Findings
Identifies four manifestations of data stewardship
Highlights the importance of AI readiness principles
Discusses challenges and opportunities in standardization
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…
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Taxonomy
TopicsLaw, AI, and Intellectual Property · Ethics and Social Impacts of AI
MethodsSparse Evolutionary Training
