Back in November, we surveyed around 200 leaders in the Student Success Collaborative about what research would be most helpful to them in leading student success initiatives in 2017. The number one topic that piqued their interest was “Data-Driven Success Planning: How to Use the EAB Institution Reports to Uncover Your Next 5 Years of Student Success Opportunities”. I was surprised by the level of interest. Many other topics generated passionate interest from corners of the membership, but only this one had such broad appeal across all institutions types—advising model, size, selectivity—and roles within the student success organization.
Of course, any institution that invests in student success analytics wants to use their student data systematically to inform decisions about how to promote student success. So the question behind the question is: Why isn’t it happening already? What stops some people from taking advantage of student data tools, like the Institution Reports, to drive their student success efforts?
Across conversations with a diverse group of student success leaders this winter, it became clear that this remains an “up-at-night” issue because people and process are at the heart of the challenge. Even the perfect data tool won’t make an impact if the people and the organizational environment aren’t right. We heard the following framework emerge: Six key ingredients must be in place for each individual to be an active and effective user of student data.
Six things you need to be an active and effective user of student data:
This one is obvious but more complicated than you might think. To use the data in their work, people need to have access not only to the tools themselves, but to the correct data for their role. Rolling out a new technology can be logistically complicated (and politically dicey if your institution is experiencing technology or initiative fatigue) but leaders need to push for broad access, especially among critical user groups like deans and department chairs.
Many people simply don’t feel compelled to use the data they have access to. Motivation is a mix of personal and contextual factors—some people intrinsically love data and some don’t. While institutions can’t change the fact that some people are data-phobes and others data-philes, they can help create the right expectations and incentives to motivate staff to integrate data into their workflows.
An Institutional Researcher once told me “I know a faculty member who doesn’t trust any data they didn’t put together themselves.” That’s a guard-rails example, but data skepticism is a common phenomenon, particularly amongst faculty. This barrier often causes some of the most capable data users to shut down and drop out of the pipeline. On the positive side, transparency and early engagement efforts have been proven effective in convincing staff that "different" doesn’t mean "wrong."
For many years, most people in student success organizations had no choice but to rely on their experience and intuition when making choices. There simply wasn’t much relevant data available to them. So, while they have access to data today, they might not see the connections between available data tools and the decisions, tasks, and work that they do across the year to promote student success. A critical aspect of rolling out a student success analytics tool is distributing use cases and success stories to help users see when they can use the data and for what purpose.
5. Data savvy.
You don’t have to be a data scientist to make data-driven decisions—but you do need to be data literate. A minimum level of comfort in accessing and interpreting the data is necessary to be able to use it effectively. This is particularly problematic in student success because the work spans academic disciplines and functional areas, meaning that the level of data savviness among potential users is inconsistent at best. Of the group of people with the access and motivation to use data, some get overwhelmed and lost when they actually dive in. Luckily, leaders can promote data literacy by creating opportunities for staff to explore the data collaboratively and engage with it.
6. Drive to action.
The last barrier is a barrier to activation. Too often, people stop leveraging data at the point of insight (“Wow, this is so interesting!”), and fail to push through and do anything with that insight. The biggest source of this inactivity is analysis paralysis: Users either can’t think of concrete next steps to take, or else feel paralyzed by all the potential actions they could take—so they do nothing. While understanding is valuable, success leaders need to push users to take action by providing guiding tools and best practices. On the frontiers of innovative practice, that sometimes means taking a little bit of a leap into the unknown and trying something.
While none of these barriers is particularly novel on its own, looking at all six together as a pipeline is valuable because it shows how they compound. Failure in any one area could be the reason why someone in your organization doesn’t use the data tools available to them. Student success leaders need to consider the entire pipeline if they are to see results. Only with all six elements in place can you hope to create a full and robust culture of data-enabled decision-making on your campus.
Which of the six failure points represents the biggest challenge that your institution must address to promote data-enabled decision-making?
Next month on the blog I’ll be doing a deep dive into one of the six failure points: Data savvy. It’s a brave new world, and having readily-available data is a relatively recent phenomenon for many roles on campus. Data literacy is one of the biggest barriers to student success, but one that you can do something about today.