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5 Questions with David Weil, Vice President and Chief Information and Analytics Officer, Information Technology & Analytics, at Ithaca College
Data is the key to all institutional analytics. Whether it’s payroll data, employee information, or data on teaching and learning, the quality of the data, its accessibility, the way it’s shared, and, of course, its governance: As has been said elsewhere, data is the new currency of higher education institutions.
According to David Weil, vice president, chief information and analytics officer at Ithaca College, universities and colleges are all at different points, or stages, of maturity in their analytics and how they use this tool to impact services across the institution.
“It all comes down to data,” said Weil in an interview with Campus Technology, “And I have thought [that] for a number of years as an IT professional. Sure. You know, we’re providing the technology, but we’re really enabling the data to flow through our organization. And increasingly, that really is a primary role of the chief information officer. And so if we think about the data: How do we use the data? How does it help inform us about student success, about our enrollment, about our financial status? And then, how do we organize around being able to leverage that data?”
Weil recently sat down to talk with Campus Technology about the evolution of institutional analytics. He will be participating in two sessions at the upcoming Tech Tactics in Education conference, being held Nov. 7–9 in Orlando, FL: A Modern Framework for Institutional Analytics and The State of AI in Education.
Campus Technology: Can you talk about the maturity model that you use or that you recommend for evaluating institutional analytics? [Weil later provided the diagrams for illustration, below.]
We wanted a way to really think about the different use cases for institutional data and what a mature program looks like. So we created a four-quadrant maturity model, which is really divided up into two broad categories, … reporting analytics. And then you have two rows, operational, strategic.
Rob Snyder, our director of analytics and special IT projects (and co-presenter for the session at Tech Tactics), provided [these examples illustrating the model’s concepts in the context of student retention]:
Operational Reporting — counting, observing, measuring — “Which students have enrolled for their third semester? Which students took a leave of absence? Who withdrew?”
Strategic Reporting — assessing, evaluating — “Which programs were these students in? What was their financial aid package? What was their first-year academic outcome?”
Operational Analytics — understanding — “How did this affect enrollment and financial sustainability, now and into the future? Are there any patterns in the students who weren’t retained? What are the profiles of these students, and are those profiles different from the retained population?”
Strategic Analytics — planning, predicting, testing — “Can we identify students who are at risk? Are the policy changes we can make to influence retention, and how would that change the prediction? Should we make changes to financial aid, and what would be the ROI? Do we need to look at offering new services and mechanisms for support?”
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