Higher education data governance sounds like a compliance topic until an AI tool is asked a simple question. One university chatbot, asked to show enrollment, reportedly counted students across every academic year and answered 600,000. The tool did what it was asked. The trouble was that "enrollment" means different things in different offices.

GovTech reported the story from a session at the EDUCAUSE annual conference in Denver, and credits the example to an earlier EDUCAUSE article. It is a useful way into a bigger point: AI exposes definition gaps that people have learned to work around.

Admissions, student services and academic operations each hold their own version of the student record. That is the territory where NSquare works with education institutions, and it is where AI questions tend to go wrong first.

Higher Education Data Governance: Why Definitions Break First

A university can use "student" or "enrollment" differently depending on context, as GovTech notes. Admissions might count a student at deposit, the registrar at a census date and finance at billing, and each office is right by its own rules. A colleague knows which one you mean. An AI tool does not.

Our ERP terminology glossary makes the same point for enterprise systems: unclear terms surface late, as rework.

What the Survey Found

The session presented findings from a survey of members of the EDUCAUSE data governance community group. Of 65 respondents, 27 said data roles were unclear at their institutions, and 18 said no one was in charge of monitoring data quality.

This is a survey of a community group's members, so it shows patterns among institutions already engaged in the topic, not the whole sector. If anything, that makes the gaps more telling.

Four Stages of Data Governance

GovTech reports that the panel organized the top ten challenges into those four stages. One panelist, who had run a governance program for three years, said most of the time it was about people, not tools.

In practical terms, the order matters. Agree what your terms mean first. Then set rules, build them into daily work, and measure what changes.

Where to Start

First, write down the definitions for the metrics your AI tools will be asked about, such as enrollment, applicants and active students. Second, name an owner for each one. Third, set data quality standards, which GovTech says should state which data is critical, what error is acceptable and who fixes it.

Fourth, write access rules that cover individual users and AI tools. For how this extends to AI agents, see our Copilot Studio implementation guide.

What We Have Seen

NSquare helps institutions unify admissions, student engagement and academic operations on Dynamics 365 and Salesforce CRM, consolidating data and streamlining workflows. In a CRM, that work comes down to one agreed definition for each core field. We do not quote education figures here, because we have none we can stand behind.

FAQs

• What is data governance in higher education?
  The roles, definitions, quality standards and access rules that decide how an institution's data is described, checked and used, including by AI tools.

  
  

• Why do AI tools give wrong answers about enrollment?
  Offices define enrollment differently. When an AI tool is asked without a shared definition, it picks one reading, which may not be the one you meant.

• Who should own data governance at a university?
  Named people for each core definition and for data quality. In the survey, 27 of 65 respondents said data roles were unclear.

• Do we need perfect data before using AI?
  No, but the definitions and access rules for the data the AI will use should be agreed upon first.