A single piece of data can reveal a lot. For example, whether colleges and universities are meeting their enrollment goals or comparing the number of merit based scholarships versus need based ones. But, there’s also a lot left out of those pictures, like how successful those students were in their classes or how long it took them to graduate – or if they did so at all.
While colleges and universities are eager to leverage institutional analytics, it is time for them to think bigger and broader. Doing so will open up a realm of possibilities not yet tapped, creating the opportunity to ask more complex questions and find solutions that better serve and support students.
Right now, according to the EDUCAUSE Data, Research, and Analytics unit, about 50 percent of institutions consider institutional analytics a “major institutional priority,” and 25 percent more report that it’s a major department-level priority. Given that using institutional analytics is at a nascent, but growing, stage, institutions are at a prime point to develop better data practices, so they avoid missing key signals that are hiding in disparate and siloed data.
Connecting the Institutional Analytics Dots
The key is moving from a narrow focus, looking at one piece of data from a single source, to connecting multiple data sources across campus to get a fuller picture.
A good example is academic insights. Many college and university leaders would love to use analytics to find the answer to “If a student earns a B in class, what’s the likelihood she’ll graduate on time?” But, academic insights like this one are just one piece of the overall puzzle. Combining curricular, co-curricular and student life variables – such as if that same student lives on campus, what level of financial aid is she receiving and what clubs she is a part of – will provide the visibility needed to fully understand the impact of the overall student experience on progression.
This broad view is a foundational component to build the analytics strategy institutions need.
Avoiding Costly Blind Spots
Without connecting these dots an institution is susceptible to blind spots that exist in the gaps of data. These blind spots arise when institutions don’t consider how every aspect of the student lifecycle – from first interest, to enrollment through graduation – impact the institution’s goals.
The cost of institutional blind spots is that policies, practices, and processes are put in place with good intentions but, due to lack of data, end up magnifying the problem, instead of diminishing it.
Avoiding these blind spots and using analytics more effectively is partly about understanding the types of questions institutions can ask, and where they need to look for information.