AI Readiness Starts with Data Governance
The education sector is moving quickly from ad hoc AI use among faculty, staff, and students to incorporate solutions for context-aware tutoring, automated curriculum tagging, and personalized advising. Among higher education professionals, 43% say their institution's strategic plan already incorporates AI, and almost 90% expect its use to keep rising through 2027.
But in the rush to adopt the technology, many institutions ignore a critical component: AI data governance — the framework of policies, processes, roles, and standards that ensures data is accurate, secure, consistent, and used responsibly.
AI data governance should be an extension of institutional data governance, not a completely separate initiative. Unfortunately, that's a problem when institutions haven't maintained their governance policies and processes — like a longstanding debt that suddenly comes due.
“In many ways, data governance is like the trusted friend organizations stopped calling because it no longer felt urgent or exciting, only to realize, in the age of AI, that it was the relationship they should have maintained all along. What many organizations are now experiencing through AI initiatives are familiar governance challenges appearing in new and more visible ways.” — Tasha Almond Schaefer, Lead Data and Analytics Consultant, and Robert Nield, Senior Data Architect at Unicon
AI as a Stress Test for Institutional Data
AI models learn from the data they're fed. When AI is deployed on top of fragmented, inconsistent, or flawed data, any issues with that data are mirrored and amplified. Models trained on incomplete or biased data can result in hallucinations, inappropriate recommendations, and inaccurate predictions — all delivered with confidence. Even a relatively small error rate in data becomes catastrophic as it compounds at scale.
For example, AI-generated outputs based on incomplete student records, inaccurate curriculum mappings, or outdated program information could lead to:
- Students receiving incorrect course recommendations that delay graduation
- Courses being tagged to the wrong competencies or learning outcomes
- Tutoring that doesn't incorporate the full academic context
Without robust guardrails for data privacy, sensitive information may also leak into prompts or outputs, causing compliance issues and undermining institutional trust and adoption.
Data Governance for AI Must Go Beyond Policy
Many institutions have committees and draft policies for AI. But data governance must be integrated into operational practices. The “top-down” approach often becomes an exercise in documentation as implementation lags due to a lack of consensus, clarity, and communication about the value, importance, and accuracy of those policies.
In contrast, Unicon's “bottom-up” approach to governance emphasizes cultural alignment, trust, and realistic progress over mandates. Collaborating with those who work closely with the data — such as data analysts, registrar staff, and IT leaders — we define stewardship roles, clarify ownership, and provide guidance on data lineage, usage guidelines, and cross-departmental collaboration to implement governance into practical workflows.
In practice, this might look like:
- Establishing clear definitions for student enrollment statuses or program codes
- Creating and managing a cross-functional team to address and resolve duplicate student records within a CRM system
- Bringing stakeholders together to standardize program mappings and course equivalencies
These efforts build the scaffolding for governance to expand organically across AI initiatives.
Just as important is building the technical foundation for AI initiatives — the “invisible infrastructure” of metadata, lineage, interoperability, validation, and standardized data models that make scalable, responsible, and effective AI applications possible. Without it, institutions may be unable to defend or explain AI-driven outputs and decisions, heightening legal and compliance risks and potentially eroding credibility.
“This is the foundational backbone for trustworthy AI systems.” — Tasha Almond Schaefer, Lead Data and Analytics Consultant at Unicon
A Practical Path Forward
Institutions do not need to address AI data governance in one fell swoop. Instead, Unicon promotes an iterative approach focused on contained efforts — such as an AI solution for a single department or function. This demonstrates value quickly and allows stakeholders to work out governance and technical issues on a small scale before expanding.
We suggest starting with a meaningful use case based on a known institutional challenge, then zeroing in on the data and pain points by asking:
Key questions to ask about your data
- Who collects and enters this data?
- Who uses it — and how?
- Where are there concerns about duplication, standardization, inconsistency, or quality?
- Where does the data flow from, and where does it break down?
With these answers in hand, we create a lightweight governance policy outlining who owns what and their responsibilities going forward. Building a prototype enables us to test outputs and make any necessary adjustments to governance before implementing and scaling the solution — so institutions can expand the framework incrementally to other AI initiatives instead of reinventing the wheel each time.
Whitepaper
Going Deeper on Data Strategy & Governance
Our whitepaper explores how institutions can build a sustainable data governance foundation, covering frameworks, roles, and the technical infrastructure needed to support responsible AI at scale.
Download the Whitepaper →AI Governance as Strategic Capability
The value of data governance is often opaque. While it may be tempting to view it as an obstacle to innovation, it actually enables institutions to scale AI innovation safely, coherently, and with less risk.
This is why governance is a critical capability for institutions in the AI era. Those that treat AI data governance as an ongoing strategic function — rather than an isolated exercise — will be better positioned to scale AI responsibly, effectively, and successfully. Output will be more meaningful and accurate, users will trust those outputs, and institutions can be confident that they are developing and implementing AI solutions that are ethical, compliant, and valuable.
