Better Data Readiness Opens the Door to Smarter AI in Higher Education

AI in higher education is everywhere—at least in conversation. But too often, the hype skips the crucial first step: data readiness.

Institutions eager to adopt predictive AI models or launch prescriptive analytics frequently overlook a simple truth. These tools are only as strong as the data, structure, and trust beneath them.

Before diving into specific tools, enrollment leaders must focus on building data readiness for AI. This goes beyond collecting data; it also requires preparation of the people, systems and workflows that will support advanced analytics.

Here’s what an institutionally ready team should be able to do before predictive or prescriptive tools are introduced:

• Enable automated data pulls across systems, rather than pulling exports manually, to ensure consistency and repeatability.
• Validate business logic by having the ability to explain why year-over-year trends shifted, such as changes in attendance or application tags.
• Possess and communicate insight fluency, so users understand what model outputs mean and how to act on them, whether that’s one central analyst or an entire admissions team.

This kind of clarity builds confidence in the data and ensures the team is equipped to act on the insights that models provide.

Predictive and prescriptive analytics in higher education both rely on one essential element: a comprehensive historical view. Having access to that information requires complete records not just of who enrolled, but also who didn’t.

Beyond that, behavioral data—how students interact with your institution—is often the strongest signal of future enrollment or retention. To make this type of data actionable, institutions should:

• Keep it granular and timestamped (one row per student per interaction).
• Add a category or type field to group similar activities, such as event registrations or email responses.

These details allow models to better understand the sequence, timing and intent behind student behaviors, thereby turning raw activity into actionable insight.

Even with the best intentions, data fragmentation can creep in and undermine models. To build a trustworthy analytics ecosystem, institutions must first understand and mitigate these three common sources of fragmentation:

• Problem-of-the-week responses, where new policies lead to ad hoc fixes without long-term tracking plans. Ask: “How will we recognize this change in our data a year from now?”
• Disconnected systems, particularly between financial aid and CRM platforms. Aligning population scope and using consistent student identifiers are essential. Collaboration helps, too. Bringing data stewards into the project early can reveal untapped or misunderstood data assets.
• Vendor churn, where changes in providers disrupt data continuity. Each transition is a chance to create explicit data crosswalks, preserving historical context and protecting against institutional memory loss.

Predictive and prescriptive analytics only deliver value when built on a foundation of trust, integration, and strategy. That means data must be relevant, historically consistent and delivered in a reliable, automated format.

It also means creating an environment where model outputs are trusted and actively used, not just reviewed. When institutions commit to data readiness, they unlock new tools while also gaining clarity, precision and a roadmap for the decisions that matter most.

This article was originally published by University Business on October 20, 2025.