Why data governance often stalls—and 8 recommendations to reinvigorate It

Higher ed leaders widely recognize that strong data governance is essential to solving institutional challenges. Without it, teams often spend more time debating whose data is correct than addressing issues like enrollment pressure, program performance, or meeting growing reporting demands. 

Yet despite this shared understanding, data governance efforts frequently stall. Competing priorities, unclear ownership, and the complexity of the work itself make data governance easy to sideline, leaving leaders without the reliable data they need to make confident decisions. The question is, how can you break out of this cycle?

Here are eight common obstacles that make data governance difficult, and our recommendations for how to respond. 

8 common data governance roadblocks 

1. Some staff do not see themselves as “data people.” 

Staff across campus, from enrollment teams to student success staff to academic leaders, depend on data every day to guide their work. Yet many of us have been in meetings where someone says “Oh, I’m not a data person.” When non-IT staff don’t see themselves as “data people,” it’s often because they haven’t been given the opportunity to learn how data connects to their jobs. 

When shaping your governance strategy, ensure that the work is clearly tied to everyday decisions in advising, scheduling, budgeting, and reporting. Staff outside of IR and IT will feel more inclined to buy into the work when governance feels practical and relevant to their daily responsibilities.

2. People fear that data will be weaponized. 

Gaining actionable insights sometimes requires sharing data between departments, but teams might hesitate to share or standardize data because they worry it will be taken out of context and used to evaluate their performance or justify resource cuts. People may also become possessive of “their” data and be hesitant to let other areas of the institution have access to their metrics. Staff need to feel confident that their teams’ data will be used ethically and in the correct context, and that data will be used to create improvements, including changes that can make work easier, rather than penalize departments.

3. Governance is treated as a one-time project.

Governance often starts with a burst of energy, but when it is treated as a one-time effort, it quickly turns into a box-checking exercise. Once those initial deliverables are done, people move on, making it hard for governance to function as the ongoing process it is meant to be.

 In reality, effective data governance must be continuously maintained, embedded into strategic priorities, and integrated into day-to-day responsibilities so it evolves alongside the institution rather than becoming static or outdated.

4. Consensus on definitions takes too long.

Teams often expect quick agreement on data definitions, only to see disagreements resurface across units and use cases. What appears to be a simple question—for example, “How many applicants do we have for next semester?” or “How much does it cost to run a Biology lab?”—might have several different answers depending on their interpretation of the question and which data is best to answer it. Because data definitions require cross-departmental agreement, and because definitions often change over time, building consensus can be challenging. When leaders set clear ownership, decision rights, and escalation paths, they create a structured definition-setting process. This allows them to gather input efficiently, avoid lengthy debates, and keep work moving, while data warehousing platforms like Edify help teams align on definitions by centralizing data across systems.

5. The scope feels overwhelming.

Governance committees often try to tackle all data domains at once, aiming to solve all the institution’s data problems from the start. This mindset is understandable, given that stronger governance can bring important improvements to each domain. But an overly broad scope may overwhelm staff who are already managing multiple competing priorities. Instead, leaders should begin with a small set of high-impact projects, especially those tied to upcoming decisions or areas of risk. Teams will be able to concentrate their efforts on the highest-priority projects, demonstrating the value of governance work and building momentum along the way. 

6. Governance is positioned as an IT responsibility. 

Governance is often framed narrowly as a technical effort or a data cleanup initiative, owned by IT teams. While IT and IR teams play a central role, this mindset runs counter to the goal of maintaining data governance long-term as an organization-wide responsibility. If only IT team members are closely involved in data governance work, many of the people closest to data creation and day-to-day use might begin to see the work as someone else’s responsibility and disengage. 

The impact of governance is most meaningful when it is positioned as shared leadership work that includes both academic and administrative teams. Leaders should emphasize collective ownership and accountability for how data is defined, managed, and used so that all staff members can understand the impact of their contributions.

7. Formal data governance changes longstanding workflows. 

When there isn’t a clear structure for managing data, people tend to figure it out on their own, building spreadsheet-based systems and sticking with processes they’ve used for years. When that’s the case, formal governance tends to feel like an added layer of work rather than something that makes daily tasks easier or more effective. This gap between policy and practice limits the impact of governance initiatives, no matter how well-designed they are. Initiatives will be far more effective when governed data is embedded directly into routine reports, dashboards, and workflows that staff already use. 

8. Progress is hard to see and sustain. 

People want to understand the “why” of data governance and how it’s going to make their lives easier. Improvements to data governance often feel abstract to those who don’t work with large data sets every day or aren’t tasked with the technical side of data management. Think of an academic advisor who spends much of their time face-to-face with students, or a faculty member whose time is mostly spent in the classroom. In this siloed environment, people might wonder if data governance initiatives are worth the work. Buy-in may erode over time, and improvements can be lost when staff leave their roles. To sustain momentum, be sure to communicate early wins and thoroughly document milestones so that progress is visible and improvements last through staffing changes.

Turn abstract goals into actionable insights 

For many staff, data governance work falls outside formal job descriptions. As a result, the work can seem abstract, risky, or overwhelming, especially when it requires coordination across units with different priorities. Addressing these common obstacles is a great way to start regaining momentum toward your data governance goals. Our latest data governance research explores seven urgent challenges data governance can help solve, including issues related to AI adoption, recruitment and retention, and reporting expectations. If you would like to learn more about the challenges and opportunities that arise with this work, you can explore the insight paper at the link below.

Tara Zirkel

Director, Strategic Research

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