Three years ago, we were in a tough spot. We had a set of ambitious retention goals to meet, we had a lot of data—albeit static, siloed, dusty data—but no “system” to collect or distribute it, much less act on it. We understood that the data at hand was informative, yes, but it was stuck in the past and not nearly reflective of our students’ lives. We knew that we had to implement new technology, but there’s nothing like the words “new” or “technology” to strike fear into the hearts of university officials.
I knew there had to be a better way. I was frustrated when I compared our own efforts to other industries like online retail. If a single click on a pair of shoes could instantly serve me Google ads and personalized Facebook posts, then why couldn’t we leverage similar data technologies for students? How could higher education be so far behind the rest of the world?
If you’re still reading, I’m guessing this scenario sounds familiar.
At that point we were entering a new relationship with Civitas, the Austin startup that allows us to apply tailored, custom student solutions, the way doctors can apply precision medicine. It was a tough sell for those of us who were championing it at the moment. Building consensus was our first challenge so we assembled a diverse, broad team, including members of our own division, academic affairs, technology and institutional research.
An institutional reorganization that brought together our institutional research and business intelligence teams set the stage for consistent and better data. So our next step as a team was to get our hands on the data. Our centralized Institutional Research team had a culture of tight control over that information. On top of that, the accuracy of the data itself was subject to question depending on its campus source. And to make matters worse, data sets were located in disparate campus departments so tracking it all down was a herculean effort. We had to launch a prolonged effort to identify, compile and enter the data into our enterprise data set. However, once our team collected data, we ran some pilot data analyses and these early succeses quickly helped us build enthusiasm!
When you begin running data analyses, you need to prepare for some surprises! After implementing our new system, we discovered that lower divisional foundational courses played a major role in predicting a student’s likelihood to graduate. We found that a C in English 101 left students with only a 41 percent chance of graduating. It was an eye-opener and challenged many of our assumptions about at-risk behavior and on-the-bubble students.
But a specific finding, even if it’s a scary one, does not mean that a department or individual is doing something wrong. It’s an opportunity—better data gives us the tools to continually improve our work! Data will test your longheld beliefs and can expose some hard truths but you can’t afford to shy away from facts. How you absorb these waves while keeping the ship afloat will determine your team’s trust and faith in data’s brave new world. Maintaining a sense of forward motion and optimism is critical.
Another challenge I see across college campuses is more insidious than fear of change; indifference to change. At Educause 2016 we talked about “initiative fatigue,” and it’s true—there will always be those who see data as yet another mandate from “the administration” or a “passing fad” in higher education, leaving many to believe that administrators won’t have the wherewithal to see it through.
That’s why you—yes, YOU—must remember that data-informed change is always, always about a student’s best interest. This is your rallying cry. At every turn, you have to make it about students.
How? When you share data, you have to make it personal. At our institution, we realized that if each academic advisor could keep just two more students enrolled then our retention would jump by four points! All of a sudden, the data didn’t seem so cold and distant. Advisors were empowered because they could actually put faces to the numbers in front of them. To borrow a phrase from Oprah, it was an “aha moment” for our team. It was at that moment that we realized the difference between data-driven and data-informed, and this nuance is a crucial hallmark of our culture.
It has taken us three years, and we’re certainly not done yet. But today, our campus serves as a proving ground for our data team and Civitas to push new limits and learn from first-generation data and insights that we actively partner to develop. It won’t be easy, but if you build consensus, earn some quick wins, prepare yourself for surprises, and always make it about the students, then your data culture will sing.
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