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

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 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.