This can help higher ed institutions perform more detailed analysis. Generalities don’t explore the nuanced race, gender and characteristics that exist — a Haitian American, for example, may be a Black American but not an African American. But by using data analysis to dive deeper than traditional broad groupings, universities can explore the variations within each category.
“Having more data enables institutions to get to that level of granularity, to be able to make meaningful inferences about the subgroups at the smallest level of disaggregation,” Pelletier says. “This is really critical, so that you’re not making assumptions about a group that really isn’t as similar as it looks in the broader data categories.”
Thanks to the increase in data surrounding the interactions and the choices students make, schools are also able to analyze and assess student pathways. With this knowledge, schools become uniquely situated to make adjustments that further enhance student success.
“There’s a lot more information around student achievement and student learning that we didn’t have access to before,” says Pelletier.
LEARN MORE: How to build a better data dashboard in higher education.
Barriers to Establishing an Effective Analytics System
While using analytics to advance DEI goals can be extremely beneficial when done right, the journey isn’t without challenges. Analytics systems can be tricky. Ethical and equitable analytics systems can be even harder to establish; in addition to being costly, these programs require institutional support that isn’t always readily given.
“Having a mature analytics program on campus is extremely difficult. Many institutions are still in the early phases of developing analytics capabilities that are both ethical and equitable,” Pelletier says. “Similarly, I think the challenge with DEI work is that it is also in its infancy. Higher education institutions are really just beginning to recognize that DEI — the D, the E and the I — are all things that are really critical for their campuses and for the students.”
At the end of the day, the data is the data. But whether this data is analyzed and used accordingly depends on people — both the analytics experts who know how to assess information in a way that takes the cultural aspects of the data into account and the institutional personnel who can enact the changes that the data analytics call for. Bringing these people together is easier said than done.
“Just bringing the right people together to speak the same language is extraordinarily difficult to do in higher ed, much less this really nuanced and sensitive work,” says Pelletier.
Regardless of the challenges, the pros of establishing a DEI-advancing analytics system far outweigh the cons, and there are a number of best practices for overcoming obstacles.
READ MORE: Higher education turns to data analytics to bolster student success.
Best Practices for Maximizing Analytics in Support of DEI Goals
Using analytics to advance DEI goals requires both analytics experts and DEI experts. Understandably, both sides won’t necessarily share the same knowledge sets, and may have difficulty transferring that knowledge back and forth for the benefit of both sides and the institution as a whole. The University of California, Merced identified a solution that can work across institutions: Insert a DEI analyst to bridge the gap between the departments.
“She has a double reporting line between the institutional research office and the DEI office,” Pelletier says, referring to how the DEI analyst enhances interpersonal collaboration. “She’s the intermediary between those two units.”
Just as important as personnel, however, is the approach they take. Pelletier explains that when working to advance DEI goals, it’s necessary to approach both the asking and answering of questions with a DEI perspective. “To do that, you really have to lean into being willing to not just use quantitative data but also work with qualitative data,” she says.
