Turning Labor Market Data Into Academic Strategy
“Community colleges are using real-time LMI and other labor market information to inform program creation and program refinement,” says Iris Palmer, director for community colleges at New America. She adds that colleges often rely on faculty expertise and employer relationships to ensure coursework remains aligned with workforce needs.
The opportunity for colleges is to move beyond using labor market intelligence solely for strategic planning and begin integrating those insights more directly into academic decision-making.
Bridging the Divide Between Workforce and Student Data
For many community colleges, the challenge is not a lack of workforce data but a lack of integration. Bringing together LMI, student outcomes data and course engagement is difficult.
Part of the problem is structural: Community colleges frequently operate separate technology environments for credit and noncredit programs, with different student information systems and learning management systems supporting each side of the institution.
“Generally speaking, they use different systems for the student information system and for the learning management system,” Palmer says.
READ MORE: Customer relationship management software can help colleges make better decisions.
That separation creates significant challenges when institutions attempt to connect labor market intelligence with student outcomes and curriculum planning.
Workforce leaders may have access to regional employment data, while academic leaders rely on information housed within SIS and LMS platforms that were never designed to communicate seamlessly with one another.
Palmer says she believes emerging AI technologies may eventually help institutions bridge some of those gaps by connecting information that remains trapped in separate systems.
“There’s a lot of opportunity to bridge these legacy systems that support the back end,” she says.
Moving Beyond Dashboards With AI-Powered Insights
While dashboards and reporting tools can provide useful information, they often answer predefined questions rather than help leaders explore new ones.
Palmer notes that institutional research offices remain understaffed at many colleges, limiting how much analysis can be performed and how quickly leaders can respond to emerging challenges.
DISCOVER: Learn how your peers are implementing artificial intelligence.
By combining student information system data, learning management system data and labor market information, institutions could identify workforce trends earlier, evaluate program effectiveness more accurately, and better understand which students are succeeding and why.
“Some community college presidents are using ChatGPT to analyze student data,” she says. “It gives them the flexibility to ask questions of the data in a way dashboards don’t.”
Why Data Governance Matters
The effectiveness of any AI or analytics initiative ultimately depends on the quality of the underlying data.
“If you put incorrect data into the model, you’re going to get incorrect answers out,” Palmer cautions.
For community colleges, data governance extends beyond data quality. Institutions must also ensure student information remains protected when shared with external platforms and analytics providers.
LEARN MORE: Is your data governance strategy ready to support artificial intelligence?
Palmer advises colleges to pay close attention to licensing agreements, security protections and privacy guarantees when evaluating vendors.
“There must be a conversation around what kind of license they’re buying, and to ensure there is a security and privacy guarantee,” she says.
Strong governance also requires consistent definitions and data management practices across the institution. Before colleges begin feeding information into AI models, they need confidence that the data is accurate and properly understood.
“It’s important to make sure your data has integrity before you start to do this analysis,” Palmer stresses.
Choosing AI Tools To Solve Real Problems
When evaluating analytics platforms and AI tools, institutions should begin with a clear understanding of the problem they want to solve.
Faculty and administrative leaders may be eager to experiment with new technologies, but IT departments should be involved early.
“A lot of times faculty and staff see IT as the people that tell them no on using new tools and experimenting with things,” Palmer says. “But they also have important information you’re not necessarily privy to.”
UP NEXT: Institutions can use data to drive decision-making.
When evaluating vendors, she recommends focusing on security, privacy protections, accuracy and the ability to minimize hallucinations and unreliable outputs.
At the same time, colleges must decide whether they need integrated platforms that address multiple use cases or specialized tools designed to perform a single function exceptionally well.
“Being very clear about your use case and what you’re trying to accomplish is incredibly important,” Palmer says.
