Data Governance: We Must Ask Questions

2026-07-09 · Source: Artificial Intelligence on Medium · Field: Business & Management — Data Science & Analytics, Operations & Process Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

The article emphasizes the critical role of asking questions in effective data governance, contrasting it with the common professional tendency to provide answers. It notes that while young children ask between 100 and 300 questions daily, this habit sharply declines in adulthood, often due to a fear of appearing incompetent, despite leaders claiming to value inquisitive minds. The author argues that successful data governance programs must move beyond assumptions by actively questioning stakeholders to understand their pain points and true needs regarding data catalogs, business glossaries, or governance initiatives. This approach uncovers hidden issues and fosters adoption, as people engage more with programs they helped shape. Furthermore, the article highlights Agentic AI as a transformative technology that can make asking questions about data effortless for business users, enabling them to interact with data catalogs in plain language and thereby building trust and engagement that traditional methods struggled to achieve.

Key takeaway

For AI Product Managers designing data governance solutions, you must prioritize question-driven discovery over assumption-based development. Your programs will gain adoption and trust when shaped by genuine stakeholder needs, not untested assumptions. Integrate tools like Agentic AI to lower the barrier for business users to ask direct data questions, fostering engagement and uncovering critical blind spots naturally.

Key insights

Asking questions, not just providing answers, is fundamental for effective data governance and stakeholder engagement.

Principles

• Curiosity drives deeper understanding.
• Untested assumptions weaken programs.
• Engagement follows shared discovery.

Method

Actively question stakeholders to uncover pain points and needs, repeating key questions until answers integrate into organizational data thinking.

In practice

• Use Agentic AI for data queries.
• Prioritize stakeholder questioning.
• Challenge untested assumptions.

Topics

• Data Governance
• Stakeholder Engagement
• Agentic AI
• Organizational Curiosity
• Data Catalogs
• Business Glossaries

Best for: Executive, Director of AI/ML, Consultant, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.