Community Conversation: How Can You Power Data-Informed Decision-Making

As institutions navigate shifting student demographics, budget constraints, and rising expectations for accountability and outcomes, data is no longer a nice-to-have resource—it’s essential. But collecting data is only part of the equation. To truly support strategic planning and day-to-day decisions, institutions need clean, consistent, and actionable data that reflects their unique context.

During JAM 2025 in Nashville, we hosted a community conversation centered on data-informed decision-making. It was an open forum for our customers to share how their institutions are using data—where they’re succeeding, and where they’re still facing challenges. From accessing accurate information during leadership meetings to teaching AI how to understand institutional terminology, the conversation touched on both the technical and cultural sides of building a data-informed campus.

We’re sharing highlights from that discussion below—not as a list of answers, but as an evolving dialogue about how institutions can better harness their data to inform strategy, improve outcomes, and build a culture of trust and transparency around decision-making.

What Is Data-Driven Decision-Making, and How Is It Being Used on Campus?

As one participant put it, making data-driven decisions means “looking at what your data is saying, understanding the relationships between datasets, and using that to find opportunities to do things better.” That sounds straightforward in theory—but in practice, participants shared that it’s often far from simple.

For one thing, institutions need faster, easier access to unified data. During meetings where decisions are being made in real time, it’s common for teams to struggle with pulling up the right information quickly. One user noted that this lack of immediate access can stall conversations or force decisions to be made without the full picture.

For another, even when data is available, different stakeholders may not be seeing the same version of it. Inconsistent definitions and siloed systems can lead to confusion or misalignment, making it difficult to move forward with confidence. There’s a growing desire for a single “source of truth” that everyone on campus can reference—one that’s accessible, timely, and comprised of unique institutional data.

How Does AI Fit In, and What Does It Need to Work Well?

When it comes to making data-informed decisions, AI has the potential to highlight anomalies and identify trends—but it needs context. That’s where prompt engineering comes in. Institutions are learning that they must teach AI tools how their data works to get accurate results. This means creating terminology parameters, starting with simple prompts, and adding complexity over time.

For example, the concept of a “registered student” can vary from one institution to another. What seems like a straightforward question (“How many current students do we have?”) can yield different answers depending on how “student” is defined. Prompting AI with specific institutional definitions helps avoid confusion. There was also interest in embedding a data glossary into systems—something many schools are thinking about but haven’t yet fully implemented.

What Makes for "Good Data"?

You’ve probably heard the phrase “garbage in, garbage out.” That idea came up repeatedly during the conversation and served as a reminder that high-quality, reliable data starts with strong data practices—not just technology.

For institutions, this means prioritizing consistency in how data is entered, how terms are defined, and how processes are documented across departments. Without this foundation, it becomes nearly impossible to trust the results that data analysis produces. Several participants emphasized the importance of defining and documenting business processes so that everyone understands their role in maintaining data quality.

Another key takeaway: it’s not just about tools and policies—it’s about people. Data stewards, cross-departmental communication, and a culture that values accuracy and collaboration are essential to maintaining “good data.” One interesting idea that was raised was for institutions to use AI not just to analyze data, but to proactively nudge users when data looks off. This could potentially help prevent poor data entry before it ever reaches a report.

What Early Wins Have Institutions Seen?

When participants reflected on the first steps their institutions had taken toward building a more data-informed culture, a few specific strategies stood out:

• Monthly “Faculty Fridays” helped build trust and transparency across departments. These monthly sessions created a consistent space to share how data was being used, answer questions, and ensure that faculty felt part of the process—not just recipients of reports.
  
  
• Data visualization tools like Power BI were praised for allowing users to explore data through filters and dashboards instead of relying on static reports. These tools empowered staff to answer new questions with existing datasets—like tracking class sizes one week and adjunct faculty usage the next.
  
  
• Consistency in terminology was identified as one of the most critical early steps. Providing clear terms alongside data helped ensure that users across departments interpreted information the same way.
  
  

Looking Ahead: Building the Culture for Smarter Decision-Making

As this conversation made clear, success in data-informed decision-making isn’t just about implementing new tools—it’s about building a culture around data that values clarity, consistency, and collaboration. The institutions making real progress aren’t just investing in software; they’re aligning people, processes, and priorities around a shared understanding of how data can drive positive change.

Many institutions are already taking meaningful first steps—whether through fostering cross-departmental conversations, regularly engaging faculty, or implementing visualization tools. As AI continues to evolve, and as expectations around data-driven strategies grow, institutions that build a strong foundation now will be best positioned to lead with clarity and confidence in the years ahead.