Data Governance Before AI Governance: The Foundation Higher Ed Can't Skip

Let me save you some costly mistakes. I've watched universities spend millions on AI initiatives only to watch them sputter because nobody could answer basic questions about their data: Where does it live? Who owns it? Is it clean enough to trust? Before your board asks about institutional AI strategy, you need a data governance framework that's actually functioning—and most campuses aren't there yet. Data governance isn't the glamorous work of AI implementation, but it's the work that makes AI possible. It means establishing clear ownership of student records, research data, and operational information. It means documenting data quality standards, access protocols, and retention policies across silos that have operated independently for decades. The institutions making real progress on AI aren't the ones with the most sophisticated algorithms—they're the ones who've finally mapped what data they have and who can use it. The practical case is overwhelming. Faculty won't trust AI tools built on questionable data foundations. Compliance officers won't sign off on AI deployments without knowing where sensitive information resides. And donors and accreditation bodies are increasingly asking hard questions about institutional data integrity. You're not slowing down AI adoption by focusing on data governance first—you're building the credibility that makes adoption sustainable. Here's what I'd tell a new VP or CIO walking into this challenge: start with a data inventory, not an AI policy. Identify your highest-value data assets and bring together the stakeholders who actually understand them. Build a small, cross-functional governance committee with real authority, not another advisory layer. This is achievable work, and the institutions doing it right are positioning themselves to lead in the AI era—while the others are still arguing about prompts.