Better Data Readiness Opens the Door to Smarter AI in Higher Education
Behavioral data—how students interact with your institution—is often the strongest signal of future enrollment or retention.
Key Takeaways
AI’s potential in higher education depends on strong data foundations (trust, structure and integration) before any predictive or prescriptive tools are introduced.
Institutions must ensure automated, validated and transparent data systems that empower teams to interpret and act confidently on AI insights.
High-quality predictive analytics require granular, timestamped behavioral data that captures both who enrolled and who didn’t.
Preventing data fragmentation—through consistent systems, long-term tracking and clear data crosswalks—ensures AI delivers real, lasting value.