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How a Data-to-Everything Approach is Transforming Higher Education

Colleges and universities store vast amounts of data, but they generally don’t do a good job of using it. Today, amid COVID disruptions, a new blueprint for IT leaders shows how universities could make better use of data to drive student achievement.

A laptop surrounded by digital icons on a blue background.
Amid COVID disruptions, a new blueprint for IT leaders shows how universities could make better use of data to drive student achievement.

The coronavirus has upended higher education throughout the country, with colleges and universities struggling to balance the values of in-person education with the need to keep students and faculty safe amid the ongoing COVID-19 pandemic. The result is an inconsistent mix of on-campus classes, online learning or a hybrid of both. And higher education institutions are struggling to ensure they have the right technology in place to provide students and faculty with the tools they need to stay engaged and on track.

The current moment has also exposed another truth about tech in higher education: Colleges and universities store vast amounts of data, but they generally don’t do a good job of using it.

A new paper, A Blueprint for Bringing Data to Every Part of Higher Education
, provides a look at the data disconnect in higher education – and shows how schools could be utilizing information they already have to improve student achievement, increase faculty and staff satisfaction, and even enhance alumni fundraising efforts.

As the new blueprint lays out, higher education institutions collect — and therefore have access to — a variety of data. They collect recruitment data when students visit their website, tour their campuses or engage with their marketing messages. Then there’s enrollment data and information from the FAFSA and financial aid applications, as well as data from schools’ financial systems, student information systems, learning management systems, alumni engagement, development programs and other disparate sources.

But schools make use of only a small percentage of the data they collect. For example, they have predominantly used data only to show historical trends, rather than assess current or future needs. Or consider student success measures: Colleges and universities traditionally have relied only on academic and curricular data to evaluate student achievement. But the emerging trend is to consider diverse data sources that could paint a more comprehensive picture of students’ backgrounds and what they are experiencing on campus. Making better use of a wider set of curricular and co-curricular data can provide a fuller understanding of the overall student and faculty experience — with an end goal to improve positive outcomes.

In addition to test scores and class grades, this wider set of data can mean everything from Wi-Fi access logs and the number of clicks on a university’s mobile app to records of bad key-card swipes and the amount of time it takes different students to register for classes. There are hidden gems that could provide meaningful insights about how a student is performing, how prepared she is and whether she might need additional support.

Citing specific examples, the blueprint shows how two large public universities – the University of Illinois and Pennsylvania State University – have overhauled the way they utilize data to drive decision-making.

Like many schools, the University of Illinois faced siloed systems that led to inefficiencies for its staff. With more than 50,000 students and 650 buildings spread across its flagship Urbana-Champaign campus, the university lacked real-time visibility into its data and had challenges accessing it to identify student challenges or measure their success.

The university implemented a new data analytics platform — which it initially relied on for data security operations and log management — to drive its Student Success Initiative. The initiative is designed to help staff understand what factors contribute to academic success and a better student experience. The university is combining data from online courses, wireless access, management system logs and other sources to build models and identify at-risk students who may need additional academic support.

The university also used data from biometric sensors to track fatigue levels in athletes on its women’s soccer team. The athletes wore the sensors during practices and completed surveys afterward. By analyzing this data, IT officials have been able to inform coaches how each type of practice affects fatigue levels in players.


The platform also has been helpful with contact tracing. After one athlete was stricken with a severe flu, the IT team used the platform to identify other athletes who had used the same public computer as the player who was ill, allowing coaches to mitigate the spread of the illness among other student athletes. This particular use case will be critical for many colleges and universities as they develop strategies to keep their communities safe and their physical campuses open during a public health crisis.

Meanwhile, Penn State used data analytics to reduce the burden on its IT support center staff, allowing them to deploy their resources more thoughtfully and more effectively. The school’s tech security operations center had only one analyst for every 24,000 students, no 24/7 staffing, and numerous inefficiencies that led to duplicated efforts. The security center needed to automate its processes, so it used an enterprise platform to overcome staffing challenges, run 24/7 security operations and streamline repetitive tasks.

Penn State’s security analysts had been manually retrieving data for an average of 150 phishing reports every day, many of which were duplicates. Analysts had to look through each submission, take action on it and provide a response to the submitter. The work consumed most of the time of two full-time staff members.

The new data analytics platform streamlined these efforts by automatically triaging known threats and capturing the source code of suspicious web pages to investigate them. The system then automatically sends a custom response to the submitter based on the specific threat identified. Along with automating the team’s workflow, the platform also stores all this data, which enables Penn State’s IT team to do further analysis to identify trends so they can be more proactive in mitigating future security threats.

By using an enterprise analytics platform, this end-to-end process now only takes up a quarter of one full-time staff member’s time, allowing the IT team to redeploy some of its resources to other mission-critical tasks.

Download A Blueprint for Bringing Data to Every Part of Higher Education to learn more.