Removing silos with data to create fully formed higher education institutions

An institution’s ability to harness and make use of its “digital exhaust” is key to building powerful engines that provide meaningful data, says Rob Robinson, senior director of strategic initiatives at Anthology.

“There are a multitude of systems that are used on campuses and that touch on all of the various processes that the institution needs to do,” Robinson says. “Whether that’s where the student engages, administrative processes, enrolment, management, library systems, card access systems…Every time there’s a transaction, it’s recorded. The real value is being able to start to interconnect those systems.”

If multiple systems are producing data that doesn’t interconnect, this data loses its potential. “The concept of the silo is there’s useful data, but it’s going to be much more useful data in this bucket when it’s talking to other buckets,” Robinson explains.

Data that shows how students engage with each other via communication tools, how often they log on and whether students are prone to steady working or cramming is all valuable, Robinson says. “There are stories to be told in that data.” Robinson explains that if data sets work together it can create longitudinal data, which helps recognise behavioural patterns that result in student disengagement. Institutions can analyse data to discover patterns and improvements across student enrolment and retention, as well as fundraising and alumni engagement, Robinson says.

However, Robinson warns that this data must only be used for good: “With great power comes great responsibility. It’s incumbent to be hyperaware of the potential to do the wrong thing.” He acknowledges that monitoring the likes of campus door access and wi-fi access points has “absolutely a creepy factor” and insists data must be handled and combined transparently, securely and in accordance with privacy laws, with a view to improve the student experience.

Robinson also warns of the potential for algorithms to introduce bias. To avoid this, he suggests reviewing the historical data used to train algorithms to eliminate inherent bias and making sure that insights are connected to appropriate and timely interventions. Traditionally, institutions analyse data at the end of term. “But those students who departed, it’s too late to help them,” he says. Instead, the goal should be to “get frequent, timely insights that are oriented to specific actions”. These could be student referrals to academic advisors or feedback to tutors when students are struggling with course materials.

Robinson says higher education institutions are already collecting data so they just need to adjust their focus to maximise the benefit. “It really is a commitment around training and change management,” he says, explaining that the software tools have become inexpensive and widely available. “The people who know how to use them can be expensive,” he says, but adds that companies such as Anthology can add value by providing systems that will talk to each other.

To make the most of data, data literacy must just be the start, Robinson says. “It’s really about helping folks understand how to ask the right questions and how to probe around for potential bias in the results they’re looking for.” He believes data literacy is just the start and that data fluency should be the goal. “In data fluency, you as an individual are capable of thinking up new ways that we could combine the data or seek new insights from it.”

By working with Anthology’s experts, higher education institutions can connect systems and translate their data into a rich resource that will help steer operations.

Find out more about Anthology.