Unlocking AI ROI in Higher Education: The Crucial Role of Data Literacy
On any given Tuesday afternoon, imagine a dean at Morgan State University effortlessly pulling live enrollment data at the click of a button—no ticket submission, no waiting period, just direct access. In contrast, many higher education institutions might take weeks for such requests. The underlying factor isn’t merely the data platform; it’s a commitment to data literacy that enables this efficiency.
Understanding the Importance of Data Literacy
Timothy Summers, the CIO at Morgan State, recognizes that the university’s strategy for artificial intelligence hinges on the staff’s ability to interpret and analyze data effectively. Without these skills, institutions risk investing in capabilities that remain underutilized. Morgan State has taken a unique approach by developing Obsidian, a sovereign AI system built by its own engineers, which learns from the institution itself and keeps a transparent record of all interactions. Notably, Morgan State is the only historically Black university on Google’s Research Technology Leaders Advisory Board, joining esteemed institutions like Stanford and Yale.
However, success in this digital landscape hinges on one critical element: a faculty and staff that can analyze, interpret, and apply data insights effectively. According to a recent EDUCAUSE report, data literacy remains one of the most neglected skills in higher education. A National Skills Coalition analysis reveals that 92% of U.S. jobs require digital literacy, yet about a third of the American workforce lacks these essential skills.
Defining Data Literacy for Higher Education
Today, data literacy transcends basic skills like using spreadsheets or reading charts. According to Summers, it entails the institutional ability to engage with data critically—questioning it, interpreting its implications, and recognizing its limitations.
As Chris Hein, a technical director at Google, puts it, it’s about distinguishing the "signal from the noise" in vast information streams, a skill that has become increasingly urgent in an AI-driven era.
Why Data Literacy is Essential for AI Readiness
With the rapid emergence of AI in education, data now serves as a foundational context, influencing decisions at multiple levels. Matt Jubelirer from Microsoft emphasizes that understanding how data is stored, protected, and structured is vital for the capabilities of AI systems.
For institutions aspiring to build robust AI infrastructures, establishing a clean data foundation is critical. Effective data governance can help secure sensitive research and student data while minimizing compliance risks associated with expanded AI use.
Summers ardently shares, “An institution that can’t read its data can’t govern its AI.” Therefore, the focus on AI readiness at Morgan State is more about institutional capacity than simply attending workshops.
Evaluating Data Literacy Across Roles
Assessing data literacy within faculty, staff, and administrative roles goes beyond simple self-assessments. It requires analyzing behavioral indicators, such as whether staff can interpret retention dashboards and evaluate AI-generated recommendations.
Institutions should also map key roles and their respective AI-enhanced workflows. The pressing question is not just “Who is data literate?” but whether these workflows rely on high-quality, trustworthy data.
Building a Framework for Data Literacy
At Morgan State, the university has developed a structured AI literacy framework consisting of three progressive tiers: "I can perform," "I can lead," and "I can decide." Other institutions, like Purdue University, are adopting similar frameworks using Google’s Gemini for Education to promote data literacy across campus.
Creating such a framework requires more than ambition; it demands a solid organizational structure to sustain ongoing literacy initiatives. Increasingly, institutions are establishing AI centers of excellence to ensure governance, alignment, and comprehensive programming that transforms data literacy from a training initiative to a core institutional capability.
Enabling Self-Service Analytics
Self-service analytics is a hallmark of empowering institutions. At Morgan State, Summers describes how enabling staff to pull reports on demand showcases the simultaneous advantages of access and literacy. Importantly, the university remotely funded its AI system through significant donations, signaling trust from stakeholders.
Robust governance underpins this accessibility. Ensuring that shared data is connected, trustworthy, and complies with privacy regulations is crucial. Institutions often face risks associated with “shadow AI tools” that circumvent these governance structures.
Measuring the Impact of Data Literacy
School leaders can evaluate the effectiveness of their data literacy initiatives by reflecting on the overall impacts of AI transformations. Are faculty spending more time on student support rather than administrative tasks? Are advisors successfully spotting critical retention signals before they lead to student dropouts?
The true ROI of data literacy emerges not on training dashboards but through tangible outcomes—students remaining enrolled due to timely interventions or administrative bottlenecks being resolved before they escalate into widespread issues.
In conclusion, prioritizing data literacy in higher education is not just a trend; it’s a pivotal requirement for thriving in today’s AI-driven landscape. As institutions continue to evolve, focusing on building a culture rich in data literacy will pave the way for sustainable success.
Embrace this transformative journey of data literacy. Together, let’s empower our institutions for a brighter, data-driven future. If you’re excited to learn more about fostering a data culture at your institution, don’t hesitate to reach out—let’s continue the conversation!
