Data Literacy Is Key to AI ROI for Higher Education

What Is Data Literacy for Higher Education Staff?

Summers says data literacy today means more than just the ability to handle the basics, especially when considering what institutions that aspire to transition into AI-driven environments actually need.

“This isn’t about knowing how to use a spreadsheet or read a chart. Those are table stakes,” he says. “What we’re actually talking about here is the institutional capacity to engage critically with data, to interpret it, to question it.” Staffers need the ability to spot “where it’s incomplete or biased and to make responsible decisions and systems that are increasingly driven by AI.”

At its core, “data literacy is the ability to make sense of all of the volumes of data that somebody might have access to,” says Chris Hein, field CTO and technical director at Google. Across higher ed staff, it’s about “being able to identify what is the signal and the noise in all of the information that’s coming to you.” That becomes all the more urgent in an AI-driven era.

Why Data Literacy Is an AI Readiness Requirement in Higher Education

With the rise of AI in the educational arena, “data no longer supports just one individual decision,” Matt Jubelirer, general manager of education marketing at Microsoft, explains. “It becomes foundational context for the next person, the next workflow.”

That governance layer matters at the infrastructure level too. For colleges and universities building or scaling AI workloads, the underlying data environment — how it’s stored, protected and structured — determines what the AI can actually do. Data protection and storage solutions help institutions establish the clean, governed data foundation that AI readiness requires, including securing sensitive research and student data against the compliance risks that come with expanded AI use.

“Data literacy now includes understanding how data is created, governed and used responsibly across the institution,” he says. “Something as simple as a confidentiality label or storage location can directly impact how data can be used across teaching, research and operations.”

Summers is highly attuned to this need. “An institution that can’t read its own data can’t govern its own AI,” he says. At Morgan State, that means building AI readiness as an institutional capability and not a training event.

“The question that we ask isn’t, ‘Did our people attend a workshop?’ he added. “The question is, ‘Can our people govern what we’re deploying?’”

Assessing Data Literacy Across Faculty, Staff and Administrative Roles

Self-assessments alone are not enough to measure institutional data literacy and need to be paired with behavioral indicators, Summers says.

“Can a staff member interpret a retention dashboard responsibly? Do they understand not just what the numbers say but what they don’t say? Can they validate an AI-generated recommendation against institutional context before acting on it?”

To measure literacy, “institutions should map key roles across campus and identify the AI-enabled workflows that matter most to each one,” Jubelirer says. “From there, the question is not just, ‘Who is data literate?’ It is whether those workflows are powered by context that is trusted, high-quality and appropriately governed.”

Microcredentialing and badging can help validate high literacy levels, with staff assigned tasks that require competency with data as part of the learning process. “They only earn those credentials by showcasing an understanding of the data,” Hein says.

Building a Data Literacy Framework: Skills, Tiers and Training Pathways

At Morgan State, Summers says, the university developed an AI literacy framework built around three progressive stages: I can perform, I can lead and I can decide. Hein points to other institutions, such as Purdue University, that are taking similar structured approaches by using Google’s Gemini for Education to establish tiers around data literacy pathways across campus.

“With a solid framework, you’ve got a little bit less of a Wild West scenario,” Hein says.

Building that kind of structured framework requires more than good intentions; it requires organizational infrastructure to sustain it. A growing number of institutions are standing up AI centers of excellence to provide exactly that: the governance structure, cross-functional alignment and literacy programming that turns a training initiative into a lasting institutional capability. Without that coordination layer, even the best data literacy frameworks tend to stay siloed in IT rather than scaling across the institution.

Data Democratization: How IT Can Enable Self-Service Analytics

At Morgan State, that framework is designed to support broader institutional access to trusted data.

Summers says that self-service analytics — such as being able to pull an enrollment report at Morgan State on demand — is what happens ”when you build access and literacy simultaneously.” The AI system was funded by an unrestricted — and historic — from billionaire philanthropist MacKenzie Scott to the university, her second donation to Morgan State.

That accessibility requires strong governance underneath it. “Data must be connected, trusted and aligned to privacy and compliance requirements,” Jubelirer says. Hein adds that institutions must ensure the shared data layer maintains both quality and security standards before broadening access across campus. Institutions that don’t build that access intentionally tend to attract shadow AI tools that bypass governance entirely and introduce the exact data privacy and compliance risks that institutions are trying to avoid.

LEARN MORE: How can you establish a rightsized higher ed data governance approach?

Measuring Data Literacy Impact and ROI

Schools can measure the impact and ROI of data literacy as it helps to drive AI adoption. The most effective way to do that “is to look at the overall impact of AI transformation across the institution,” Jubelirer says. “Are faculty spending more time supporting students and less time on administrative work? Are advisers identifying and acting on risk earlier?”

The return on data literacy “doesn’t show up in a training dashboard. It shows up where the return on AI shows up,” Summers says.

“It shows up in a student who stays enrolled because an adviser caught a retention signal three weeks before it became a dropout. It shows up in a financial aid office that identified a processing bottleneck before it created 400 calls in a single week,” he says.

By measuring those outcomes, schools can demonstrate the impact of data literacy in driving ROI around AI.