Higher education’s approach to technology can be frustrating at times.
The arrival of much-hyped technologies to higher ed is often met with a skeptical side-eye, perhaps some cautious optimism without any action or, at most, a single toe dipped into the pool. Colleges and universities are almost never early adopters of new tools, and even with well-established technologies, higher education often lags behind.
Some of that is out of necessity, for financial or staff-related reasons, but some is by choice: Institutions that can trace their roots back centuries tend to feel more comfortable with a deliberate approach than a startup might be.
The exception is the cloud. According to the 2024 CDW Cloud Computing Research Report, nearly 59% of higher education institutions have moved at least half of their apps to the cloud, compared with just 45% of overall respondents. So, while a wholesale cloud migration is rare but not unprecedented in higher education, universities have embraced and engaged with the cloud in intentional, meaningful ways to help students.
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Understanding Which Apps Belong in the Cloud (and Which Don’t)
Higher education’s widespread philosophy of approaching new technology carefully is almost tailor-made for cloud migrations. Not large ones, of course, but the kind of piecemeal, one-application-at-a-time migrations that already seem to be popular among college and university IT leaders.
In higher education, ecosystems are complex, sometimes with siloed schools of study, research offices, administrative departments and more all coming together under a single, institutionwide IT department. So, being intentional about which cloud-based apps will benefit students, faculty and staff and which won’t is an important decision.
Sometimes, the decision gets made for universities. While migrating research computing to the cloud is, again, not unprecedented, it is unusual, and in some cases it’s not even an option. A number of the grants that R1 research institutions compete for mandate on-premises data storage, at least for now. Beyond just research, compliance rules may also cause colleges and universities to stick with on-premises data centers because they allow compliance to be monitored more easily.
However, a lot of what ends up in the cloud in higher education isn’t about data storage and isn’t obviously identifiable as cloud consumption. Cloud-based applications and Software as a Service tools, including learning management systems, customer relationship management platforms used to track the student lifecycle, productivity suites and cloud contact centers are more commonly used in higher education. And once those apps are in the cloud, modernization becomes simpler for understaffed IT teams to execute.
In general, applications should be moved to the cloud when it makes good business sense, and often, removing on-premises infrastructure does just that. For example, by using the Microsoft 365 SaaS suite, universities shrink the physical size of their data centers by removing Exchange servers, file servers, the Dynamic Host Configuration Protocol server and more. Institutions not only save space by removing the servers, they also save down the road on energy costs, cleaning costs, maintenance expenses and more.
One option for universities looking to move applications to the cloud is a thorough assessment of your application environment, such as the Strategic Application Modernization Assessment offered by CDW. Through that process, colleges will gain a full understanding of their application portfolios and receive recommendations for what could and should be hosted in the cloud.
Assessing the Future of Cloud Computing in Higher Education
New and updated applications are being designed to run in the cloud, which means cloud adoption rates across industries are likely to increase, and artificial intelligence is poised to be one of the drivers of that transition.
We don’t anticipate higher education becoming a leading adopter of AI tools, in keeping with its deliberate overall philosophy on tech, but colleges and universities will remain among the leaders in developing new AI tools. That development is going to take storage and robust computing power through graphics processing units and tensor processing unit processors that could be cloud-based.
Cost will also be a factor, as always, and while the cloud can provide cost savings, it’s not the cheapest option in every instance. Engaging with a FinOps practitioner, such as those at CDW, can help institutions better understand their financial reality in the cloud even more clearly.
UP NEXT: What should higher education know about Artificial Intelligence as a Service.
This article is part of EdTech: Focus on Higher Education’s UniversITy blog series.
