Author: Stefaan Verhulst Date: January 10, 2026 Source: arXiv:2601.06687

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.

Key Argument

The paper proposes strategic data stewardship as a complementary institutional function designed to systematically, sustainably, and responsibly activate data for public value.

The Data Winter Problem

• Not a shortage of data itself, but restricted pathways for legitimate reuse
• Conventional governance approaches focusing on compliance, risk management, and internal control are insufficient
• Data often left technically available yet practically inaccessible

Traditional vs Strategic Data Stewardship

Unlike traditional stewardship, which tends to be inward-looking, strategic data stewardship focuses on:

• Enabling cross-sector reuse
• Reducing missed opportunities
• Building durable, ecosystem-level collaboration
• Translating governance principles into actionable practices

Framework Components

The paper outlines:

1. Core Principles — foundational values guiding data stewardship
2. Operational Functions — practical activities stewards perform
3. Competencies — skills required for effective stewardship
4. Data Stewardship Canvas — practical implementation tool for adoption across contexts

Application Contexts

The framework supports adoption across:

• Data collaboratives
• Data spaces
• Data commons

Relevance to AI

Strategic data stewardship becomes increasingly vital in AI contexts, where governance principles must translate into actionable practices that:

• Build ecosystem-wide trust
• Ensure data serves public interests
• Enable responsible AI development through proper data governance