Even the best-designed data governance or data intelligence initiative will encounter resistance. Understanding the specific barriers that each role faces—and recognizing them early—is critical to keeping your initiative moving. This blog post explores the common barriers to data governance buy-in across the various roles involved in a typical initiative.
This is the second post in a three-part series. The first post covers why buy-in matters and why it must be built across roles. The third post addresses strategies for overcoming these barriers.
Data governance is not the responsibility of any single person or team—it spans the entire organization. While specific titles vary by organization, the following general role categories are typically involved:
Organizational Leadership: Executives and senior leaders who provide financial support, set priority, and signal organizational commitment to the initiative.
Data Governance Champions and Committees: A small, dedicated group—ideally three to five individuals—who lead, plan, and facilitate the initiative.
Data Stewards and Subject Matter Experts: The people closest to the data in their functional areas. They are the primary curators of data-related content and the go-to experts when questions arise.
Data System and Tool Owners: Those who manage the technical systems and reporting tools that house or surface data. Their cooperation is essential for metadata access and integration.
Analysts and Report Creators: Staff who build reports, integrations, and ETL processes. They generate significant knowledge about how data is used and calculated.
Data Consumers: Staff across the organization who use data and data-related content to make decisions, run reports, and do their jobs.
AI Data Analysis Tools: Increasingly, AI-powered reporting and query tools are participants in the data governance process—they need guardrails and training just like human report writers do.
Organizational Leadership
Leadership is often the first hurdle. Without executive support, it is difficult to secure the time, budget, and organizational priority that data governance requires. Common barriers include:
Limited understanding of what data governance involves and what it delivers.
Difficulty connecting the initiative to strategic organizational goals.
A default posture of protecting limited resources—when in doubt, leadership tends to say no.
Competing priorities that make data governance seem non-urgent.
Data Governance Champions and Committees
The people championing the initiative often face their own set of obstacles:
Competing goals and lack of clarity on where to start.
Champion groups that are too large—a committee of 16 data stewards make it difficult to move quickly or make clear decisions.
Committees that try to do governance work in meetings, which wastes time and frustrates participants.
Unclear role definitions that leave committee members unsure of what is expected of them.
Data Stewards and Subject Matter Experts
Data stewards are essential to populating and maintaining your data governance content—and they are frequently the group with the most resistance. Barriers include:
Being asked to take on this work without it being part of their formal job description.
A perception that data governance tasks are busy work disconnected from their real responsibilities.
Being asked to write large quantities of content upfront - "go write 100 definitions"—without a clear reason why that content is needed now.
Underestimating the true effort required and then feeling overwhelmed once they commit.
No guidance, training, or support for how to do the work they are being asked to do.
Data System and Tool Owners
These individuals control access to the metadata and technical content that data governance programs depend on. Their buy-in is frequently difficult to secure because:
Their first instinct is often to say no to new access requests, particularly from outside their direct chain of command.
They may report to a different part of the organization than the data governance team, leading to mixed messages from leadership.
They have legitimate security and compliance concerns about opening up access to data model or system metadata.
There may be territorial concerns - fear that opening up this access will reduce their perceived value or expertise.
Analysts and Report Creators
Analysts and report writers are key generators of organizational data knowledge—but they often resist participating in data governance because:
They lack training on how they fit into the data governance process.
They feel that documenting their work takes time away from their primary responsibilities.
Past experience with documentation efforts that went nowhere makes them skeptical.
When they reach out to subject matter experts with questions, slow or nonexistent responses push them to simply make up assumptions—which leads to inconsistent reports and eroded data trust.
Data Consumers
If data consumers cannot find or trust data governance content, the initiative ultimately fails at its core purpose. Their barriers include:
It is not obvious where to go or how to find information about a report or data element.
When content does exist, it may not be written in terms they understand.
When relevant content does not exist, there is no easy way to request it.
When a request is submitted, responses are slow, off-topic, or nonexistent.
AI Data Analysis Tools
AI-powered reporting and query tools are increasingly common. They face the same underlying challenge that human report writers face—without guardrails and validated training data, they will produce inaccurate or inconsistent results. The barriers here are questions that many organizations are still working to answer:
How do I provide guardrails and validated training for these AI tools?
How do I get the AI tool to follow data governance rules and definitions?
How do I know if the AI tools have used the guardrails or not?
How do I validate content created by AI without guardrails in place?
These are not rhetorical questions. They represent a real and growing gap in most data governance programs. Addressing them requires intentional integration of your data catalog content into your AI tool workflows.
Looking across all these roles, a few patterns emerge. People resist data governance participation when:
They do not understand why they are being asked to do something.
They believe the effort required outweighs the benefit they will receive.
They are asked to do work that feels disconnected from their actual job.
They have had negative or unproductive experiences with governance activities in the past.
Recognizing these patterns is the first step toward addressing them. The third post in this series covers specific strategies for overcoming each of these barriers by role.
For a deeper dive into this topic, we encourage you to watch our recorded webinar "Building Buy-in and Adoption for Data Governance", which covers buy-in strategies, role-specific barriers, and practical guidance in detail.
You may also find our blog post "Adoption and Return on Investment are Important for Data Governance. Here are Some Resources" helpful as you build the case for buy-in within your organization.
Hope this blog post was of assistance to you and your organization. All our data governance and data intelligence resources (blog posts, videos, and recorded webinars) can be accessed from our data governance resources page. IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship, artificial intelligence, and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
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