A data governance program is the operating system for your company’s data – the formal framework of people, processes, and policies designed to make sure that data is high-quality, secure, and accessible. While Collibra is the platform where these rules are automated and stored, the program is the strategy that defines roles, standards, processes, and compliance.
Adopting Collibra and data governance go hand in hand – but what happens when people seem to be using Collibra, but data governance adoption is still low? Let’s talk about it.
We’ve talked about the pitfalls of Collibra adoption before, but data governance adoption is an entirely different topic. Or perhaps not different, but much wider in scope, with Collibra being a tool to operationalize data governance and execute its workflows – but not substitute it.
The global data governance market is projected to grow from USD 5.38 billion in 2026 to USD 24.07 billion by 2034, with a CAGR of 20.50% during the forecast period. And while, according to studies, the key driver seems to be regulatory compliance (next to AI readiness), we want to argue that when it’s the only reason for introducing a data governance program, its chances for successful adoption are slim.
And that’s because a prerequisite for long-term data governance adoption is usually the user experience – how easy data governance workflows are to use in reality, and the value people get directly from it.
When that’s made clear, it’s much easier to introduce a data governance model that sticks. In this article, we’re talking about the difference between introducing Collibra and a data governance program in an enterprise organization, and how to increase data governance adoption across teams.
Key takeaways
- Software is an enabler, not a solution. High Collibra usage doesn’t automatically mean your data is governed.
- True data governance requires a shift in mindset and behavior, not just filling out forms in a UI.
- Distinguishing between tool engagement and business value is critical for long-term success.
- Moving from compliance-based to value-based governance is the right path to data governance sustainability in the long term.
Why do data governance programs fail even when software usage is high?
If you looked inside enterprise data governance programs across organizations, you’d see a common paradox. Collibra dashboards might show thousands of logged-in users and tens of thousands of ingested assets, but the business could still struggle with data silos and untrustworthy reports.
That’s because high software usage can still mask data governance that doesn’t work the way it’s intended to.
How is that possible? There are several reasons, but let’s take a look at the most popular culprits.
The “Zombie Governance” phenomenon
Zombie governance happens when users are active in Collibra – they could be moving workflows along and clicking “approve” buttons – but the underlying data remains stagnant or poor in quality.
The “zombies” are going through the motions because they were told to, but the data culture is pretty much dead. People might be filling out fields with “n/a” or “tbd” just to skip mandatory fields and get a workflow moving. But the data’s obviously incomplete that way, and assets are cataloged, but not curated. Plus, even though roles might be assigned, no one takes true ownership of the data.
The “Checkbox compliance” trap
When data governance is framed purely as a regulatory burden, it falls into what we call the checkbox trap.
Employees will populate the Business Glossary or map lineage only to satisfy an audit requirement. And because the focus is on the tool’s completeness rather than its usefulness, the enterprise ends up with a perfectly configured environment that provides zero competitive advantage and no clear value to the user.
Not seeing the value is also one of the most common causes of failed Collibra adoption – we talk about it in this article: Why Collibra adoption fails (and how to fix it in 2026).
What’s the difference between Collibra adoption and Data Governance adoption?
In general, we’re talking about adopting the Collibra tool and using it daily across the enterprise versus following clear data governance processes and workflows. Yes, both are tied together, and Collibra is built to encourage and facilitate data governance, but it won’t work if data governance best practices are not embedded in the everyday work of people using it. It is, after all, “just” a tool (even if a powerful one).
Let’s look at the difference in terms of some of the important metrics.
Metrics for data governance adoption
- Data trust levels: Are analysts spending less time cleaning data and more time analyzing it?
- Time-to-insight: Has the time required to find and gain access to a dataset decreased?
- Policy compliance: Are data privacy and security standards being met naturally during the development lifecycle?
- Issue resolution speed: How quickly are data quality issues identified and remediated?
Metrics for Collibra adoption
- Monthly active users (MAU): How many people are logging into the platform?
- Asset enrichment: What percentage of assets have descriptions, owners, and tags?
- Workflow completion: How long does it take for a “New Term” request to move from draft to approved?
- Search queries: What are users searching for, and are they finding it?
Data governance adoption vs. Collibra adoption
| Feature | Collibra adoption | Data Governance adoption |
| Primary focus | The Tool: Features, workflows, and technical usage. | The Culture: Behavior, decision-making, and data literacy. |
| Success metric | High login rates, asset count, and task completion. | Reduced data silos, high data trust, and faster time-to-insight. |
| Driven by | IT, Data Architects, and Tool Administrators. | Business Stakeholders, Data Owners, and Executive Leadership. |
| Daily activity | Logging into the portal to tag assets or approve a workflow. | Using governed data to make a strategic business decision. |
| “North Star” goal | A perfectly mapped and populated Data Catalog. | A company where everyone knows how to find, trust, and use data. |
| Risk of ignoring | The software becomes “shelfware” – expensive and unused. | The tool is used, but the data remains poor and untrustworthy. |
What are the risks of prioritizing the tool over the culture?
If you prioritize Collibra configuration over a bigger organizational cultural change, you risk building a kind of data ghost town. Eventually, the business will see the software as a cost center rather than a value driver. And the whole point of introducing a data governance tool like Collibra is to provide long-term value.
And, most of all, you risk things like:
- Low ROI, which is pretty self-explanatory. You’re wasting expensive licenses on users who don’t see the benefit.
- Users become frustrated with additional bureaucracy and find ways to bypass official channels. Which, again, makes no sense for the organization in the long run.
- Over-customizing the tool to fit broken processes ultimately leads to technical debt, making future upgrades and scaling difficult.
How can enterprises drive true data governance adoption beyond the software?
To move beyond the software, you have to focus on the user experience for data governance overall. This usually means embedding data governance best practices and workflows into everyday work – with minimal friction.
Often, this also means adopting a product mindset for data governance – complete with user personas, pain points to solve, and feedback loops.
True adoption also means users don’t necessarily have to go to Collibra to benefit from it. The governance should meet them where they already are, for example, via integrations with BI tools. Anything that lowers the friction and reduces the number of clicks to see the value.
Shifting the pitch from “You must comply” to “This makes your job easier.”
Speaking of value, we’d argue that having to comply with regulations is not a good enough reason for people to actually start using data governance workflows and tools like Collibra. More than anything, it’s a threat.
The real question to answer is – what’s in it for them?
Most people don’t care about data stewardship. But they do care about being able to trust their Monday morning reports. So, instead of telling a Data Steward that they have to document 50 terms a week, show them how documenting those terms reduces the 50 Slack messages they receive asking, “What does this column mean?”
True adoption happens when people realize that data governance brings tangible benefits – not just for the business, but for their everyday work, making tasks easier, and outcomes faster and more predictable.
How does Murdio bridge the gap between software configuration and cultural change?
At Murdio, we believe that Collibra is the engine, but your data culture is what fuels it. We’ll configure your operating model, but we don’t just walk away when we’re done. We’ll also align the platform’s technical capabilities with your organizational goals by working on:
- Defining value-based use cases like GDPR compliance or marketing attribution rather than just “boiling the data ocean.”
- Designing Collibra workflows that mirror how your people actually work, reducing friction and increasing natural engagement.
- Training and communication strategies necessary for successful adoption and building a truly data-driven culture that’s more than just a buzzword.
So, is your Collibra environment thriving or just surviving?
Contact Murdio today to learn how we can help you drive meaningful data governance adoption.
Frequently asked questions (FAQ)
It’s possible. Users may be active in the platform simply because they’re required to. But if that activity doesn’t result in better data quality or faster decision-making, you’ve adopted a software tool, but you haven’t yet adopted a governance mindset.
Also, look at your data requests. If users are still bypassing Collibra to ask “Where can I find this data?” on Slack, or if your Data Stewards view their governance tasks as a “tax” on their “real job,” you are likely focusing on compliance rather than value.
While IT often owns the technical configuration and maintenance of Collibra, the Business should own the adoption (or ideally, both should work together). Cultural change only happens when department heads and data owners see that governance solves their specific problems, like inaccurate quarterly reporting or slow project delivery.
Absolutely, and it’s often recommended. Establishing clear roles, responsibilities, and a data-first mindset manually makes the eventual transition to Collibra much smoother. The tool should scale your existing success, not be expected to create it from scratch.
Focus on a high-pain use case. Identify a specific report that the executive team relies on that is currently difficult to produce or viewed as untrustworthy. Use Collibra to govern that specific data lineage and quality. When that report becomes reliable and push-button ready, the value of the entire program becomes undeniable.
