Higher ed is sitting on mountains of data about student finances, program costs, enrollment and recruitment, student behavior, retention, graduation rates, and more. But institutions don’t know what to do with it. Skills gaps, data silos and limited budgets to invest in modern systems are long-standing barriers to colleges using their data effectively. The explosion of AI in platforms across campus and new demands for workforce data from federal and state agencies mean an urgent solution is needed.
At ASU+GSV, a huge convening of educators, investors and ed-tech entrepreneurs this month, every conversation I had was dominated by AI. It holds enormous possibilities to improve institution advancement, the student experience and student support. And most student information systems, customer relationship management platforms and learning management systems workflows are already infused with agents and AI-powered systems.
Yet the promise of those tools is only as good as the cleanliness and accuracy of the data flowing into them—and the skills of humans to analyze the results. Bad data in + bad data out = bad decisions all around. Too many institutions are in this situation. Just 1 percent of Educause community members surveyed said their institution’s data systems are fully modernized, and about two-thirds (68 percent) said some systems were modernized, while another third said they were either in early discussions about data modernization (24 percent) or not addressing it at all (7 percent). In short, most colleges are buried in data they can’t fully use.
It’s what Mark Milliron, president at National University, calls “bad plumbing.” The team at National spent a year establishing inclusive data governance and building a comprehensive data warehouse, including mapping their data and cleaning it (for example, making sure entries are consistent and in the right format) before they integrated it into their software systems. “I don’t think we could’ve scaled some of the strategies we’ve done unless we did the plumbing work up front,” he said in a panel at our Student Success event a few months ago.
National’s example could hold lessons for other institutions. Fixing the data plumbing will look different for each college, but getting their data—in areas such as student performance, enrollment and program costs—in order is essential for institutions before they sign software deals with eye-watering price tags. Administrators who are data literate will know how effective a platform is when it’s not talking to other tech systems, and they can pass on good data skills to new employees. That foundation could even put institutions on the road to greater tech autonomy.
Now that National has improved data skills across teams, Milliron said they’re moving into design thinking and “domain expertise” —a deep understanding of National’s nontraditional, working and military students— to create bespoke systems for their campus. Every college across the country has that domain expertise when it comes to its own students. With clean data systems and greater data literacy, colleges could create their own AI-powered platforms more easily than ever before.
These data-management problems don’t just plague institutions. They extend up, leaving federal and state governments in the dark. The sector doesn’t have the right data about degree holders and industry jobs in the right places to react to labor market demands or to track the return on investment for individual programs.
In a session at ASU+GSV, Chris Mullen, the strategy director for data and measurement at the Lumina Foundation, gave an early preview of a new project to bring the core buckets of federal data—collected under the Workforce Innovation and Opportunity Act and the Perkins Career and Technical Education Act, as well as the Registered Apprenticeship Partners Information Database System and the Integrated Postsecondary Education Data System—together to give states and the federal government real-time information on talent pipelines and industry demand.
The stakes are about to get higher. Linking education and labor data will become critical for Title IV funding under new program-level accountability measures rolling out July 1. As they stand, “do no harm” regulations require data sharing between institutions, states and the federal government to understand if undergraduate programs are producing graduates earning more than a local working adult with only a high school degree. Graduate programs will be judged against the earnings of bachelor’s degree holders. Workforce Pell eligibility will require similar tracking from providers and state leaders before funding will start flowing.
The effort to link data across agencies might sound herculean, but it isn’t unheard-of. In the ASU+GSV session, Kristin Hultquist, the CEO and founding partner of HCM Strategies, pointed out that IRS data feeding into federal student aid applications made the FAFSA process more accessible to more students. Lumina’s proposal would “reimagine the federal-state data partnership,” she said, and reveal what data is lacking and where it should be cleaned up. Arkansas, she added, is an example of a state that is already blending data well. Its LAUNCH initiative links job-seeker and employer data in a free, AI-powered platform to address the state’s workforce needs.
The data in higher ed exists. The tools to use it for real, evidence-based decision making are here. What’s missing is the plumbing to connect systems and the investment in the unglamorous work of cleaning the data that flows through them. It’s difficult but it’s urgent: You can’t build an AI-powered future with dirty data and broken pipes.
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