Data Fluency as a Strategic Imperative

A Conversation with Ellen Wagner

It’s been decades since higher education leaders and analysts first alerted institutions about a “data tsunami” that would challenge both infrastructure and academic programs. How would legacy data centers keep up? And how would curriculum in most disciplines, as well as research and administration change?

We asked Ellen Wagner, predictive analytics reporting (PAR framework) pioneer, seasoned consultant to higher education, and perhaps the ultimate digital learning activist, for a sense of where we’ve been on our data journey, and for a structured view of the data capabilities higher education institutions can foster for success.

Mary Grush: What was it like to be in the education data trenches “back in the day”?

Ellen Wagner: There were many people, including me, engaged in an extensive conversation about “big data in education” — which was, notably, a national trend fueled substantially by the Bill & Melinda Gates Foundation. The idea was to focus on student success and leverage the growing interest in big data to explore how to address retention, progression, and completion problems. There were maybe 7 crucial years — from about 2009 until 2016 or so — during which predictive analytics matured enough not to be seen as “the devil’s work” in education!

There were maybe 7 crucial years — from about 2009 until 2016 or so — during which predictive analytics matured enough not to be seen as “the devil’s work” in education!

Grush: Obviously there were many trials and discoveries over the years, but could you pick one lesson learned about the value and role of data — one that was perhaps the most important?

Wagner: Sure. Data are the means of achieving various ends, through analysis and decision making. Being clear about what your ends are — and finding the essential information needed to support the search for answers about how best to achieve those ends — is what’s truly important.

Data are the means of achieving various ends, through analysis and decision making. Being clear about what your ends are — and finding the essential information needed to support the search for answers about how best to achieve those ends — is what’s truly important.

We obtained insights from exploring data patterns; we evaluated the tenability of our hypotheses. We tested our assertions over and over again to ensure that tenability. But then, we still needed to figure out what we were going to do, actually, with the information we learned, in terms of supporting students better.

So, it ended up not really being ‘all about the data’ — or having the ‘most’ data…

Grush: …It’s about clarity.

Wagner: Yes.

Grush: And to bring up a more current issue relevant to our data policies and practices, today we often hear a plaintive cry in higher education, especially in instructional circles: “Am I losing my expert status to AI?”

Is this a concern we will one day resolve?

Wagner: I do think that a very important question on people’s minds these days revolves around the accuracy and efficacy of student performance data along with the role that AI is and/or isn’t going to play in figuring this out for institutions.

A very important question on people’s minds these days revolves around the accuracy and efficacy of student performance data along with the role that AI is and/or isn’t going to play in figuring this out for institutions.