7 Reasons Why Data Governance Projects Fail (And How To Avoid It) 

“Where can I find up-to-date customer data?” “What does this column mean?” “Where did this value come from?” – if these or similar questions keep coming up more often than they should, it’s not a coincidence. It’s a sign that your organization lacks a consistent approach to data management. Because today, data is not just about tables, ETL processes, and data warehouses. It’s the foundation for decisions, operations, and strategy – and often the source of daily dilemmas for users, uncertainty for analysts, and frustration for managers who need facts, not assumptions. 

Based on our experience and conversations with participants of our webinars one thing is clear: most organizations understand the growing role data plays in shaping and executing business strategy. And yet, questions about data sources, definitions, and quality are still part of everyday conversations. That’s not a coincidence. These questions are symptoms of deeper issues in the data environment and they’re just the tip of the iceberg. Beneath the surface lie outdated or missing documentation, unclear processes, lack of data ownership, low awareness of data’s importance, and technology disconnected from actual business needs. 

Many of these challenges can be addressed by implementing Data Governance and Data Management. But will completing a project make all our data problems disappear? Not necessarily. Let’s take a closer look at the common pitfalls organizations face when trying to bring order to their data – and how to improve the chances of success for any Data Governance initiative. 

Here’s our list of the seven deadly sins, and proven ways to avoid them. 

1. Treating Data Governance as a “one-and-done” project 

Data management is often treated like a typical system implementation – something to deliver, check off, and move on from. But Data Governance doesn’t have an end date. It’s a continuous process that should evolve alongside the organization, responding to changing business needs, team rotations, and new data sources. When that’s missing, a familiar scenario unfolds: documentation becomes outdated, data owners vanish, and questions like “What does this field mean?” return like a boomerang. These symptoms: lack of ownership, inconsistent data, unclear sources were highlighted by participants in our recent webinar as some of the biggest pain points in daily data work. 
 
What to do instead 
Design Data Governance as a permanent part of your organizational ecosystem with clearly defined roles and structure, regular review cycles, update processes, and performance metrics. Think of it as the nervous system of your data organization: always monitoring and responding to change. Set clear rules that remain in place regardless of staff turnover or shifts in technology – this is how you avoid chaos and maintain trust in your data.

2. Seeing data problems as just an IT issue 

Data Governance fails to deliver when data responsibility falls solely on the IT department. In many organizations, data is still viewed mainly as a technical asset – something to “maintain,” “process,” or “integrate.” But in reality, data is the foundation for business decisions which means the business should share responsibility for its quality, meaning, and relevance. 

Data Governance projects are often mistakenly owned by IT. Data is treated as a shared resource, but surprisingly often, no one actually owns it. No one wants to take responsibility, or even knows they should. The result? IT implements tools, but the business still doesn’t trust the reports because it’s unclear how the data was calculated, when it was last updated, or whether it’s even complete. 

What to do instead 
Start by identifying how data is used across your organization and clearly define who manages it, and who uses it (and how). When defining governance structures, ensure proper representation from both IT and business teams. Together with business stakeholders, define and implement Data Owner roles – people responsible for the meaning, quality, and business relevance of the data. If possible, assign these roles to those who have already been acting as informal owners. It’s Data Owners who should define what counts as an “active customer,” how to calculate “adjusted revenue,” and which data is critical to specific operations. The role of IT doesn’t disappear in this model – quite the opposite. IT becomes a technology partner, providing tools, ensuring data security and integration. But it’s the business that gives data its meaning and takes ownership. 

3. Lack of documentation, glossaries, and business context Plain Sight 

If no one can clearly explain what “customer status” means or how “adjusted revenue” is calculated, it’s hard to trust the data. Without documentation, glossaries, and metadata, data becomes guesswork and guesswork is a weak foundation for business decisions. Questions like “How do we calculate this field?”, “What does this column mean?”, or “When was this data last updated?” come up surprisingly often in many organizations. Not having answers is more than just an operational issue – it’s a serious barrier to building a data-driven culture. 

What to do instead 
Make data information clear and accessible. Create and regularly update both technical and business documentation, including glossaries, term definitions, and metadata descriptions. Leverage the power of generative AI: language models (LLMs) can significantly speed up documentation efforts, help translate technical language into business-friendly terms, and automate updates in data catalogs. The more understandable the data is, the more likely it is to be adopted and trusted. 

4. Focusing on tools instead of processes 

Modern tools for data cataloging, quality management, or metadata management offer exciting capabilities. But if choosing a platform becomes more important than defining who will use it and how – that’s a red flag. Even the most advanced solution won’t work without the right procedures, roles, and rules to support it. Organizations often invest in technology hoping it will “fix” issues with data quality, ownership, or documentation. In reality, this often leads to underused tools and disappointment because tools alone don’t create order. It’s the processes that do it. 

What to do instead 
Start with the basics: before introducing new tools, design the processes that will support them. Who approves changes in the glossary? How are new data sources submitted? What does onboarding a new user to the data catalog look like? These questions need clear, well-defined answers. 

5. Trying to fix everything at once 

Many leaders kick off their Data Governance initiatives with big ambitions: “Let’s clean up all our data,” “Let’s do it once and do it right.” It sounds great in theory. In practice, it leads to team overload, scattered efforts, and a lack of visible results. Without priorities, a clear MVP, or quick wins the project loses momentum and engagement drops. This issue came up during our recent webinar as one of the most common reasons projects “go off track”: too much scope, too little focus, and too long a wait for the first tangible outcomes. 

What to do instead 
Start by setting a priority (yes, singular – that’s intentional 😉). Choose one area that brings real value to the business, for example, customer data, sales data, or information used in management reports. Focus on cleaning up just that slice: define terms consistently, describe the metadata, and make sure users understand what they’re working with. This way, you’ll see results faster and that data will become clearer, easier to use, and more trustworthy. 

6. Vague goals and no success metrics 

“Let’s get our data in order” sounds good but doesn’t say how, or how we’ll know if we’ve succeeded. Many Data Governance initiatives start with good intentions but without clearly defined goals. The result? It’s hard to measure progress, hard to keep people engaged, and even harder to justify continued investment. Many of us face very specific problems – from missing or outdated documentation to situations where finding answers to basic business questions (like how a metric in a report is calculated) takes far too long. Each of these challenges can be turned into measurable progress indicators and that’s exactly where planning your Data Governance efforts should begin. 

What to do instead 
Start with specifics. If you want Data Governance to deliver real results, you need to know how to recognize them. Identify the key challenges your business is facing and focus on solving those first. Don’t try to fix everything at once – that’s the fastest path to derailing the entire initiative. When setting goals and defining success metrics, make sure the focus is on the business value of data, not just technical outputs. It’s not about how many tables have been documented – it’s about whether the data has become more accessible, understandable, and actually used. Ask yourself: how quickly can a user find the definition of a specific metric today? How many Data Owners truly understand their responsibilities? Is the number of report-related error tickets going down? These are the kinds of measurable signals that show your Data Governance is working and worth continuing to invest in. 

7. High entry barriers and low adoption 

Even the best data catalog won’t help if users don’t know how to use it, or worse, are afraid to ask. When documentation is unclear and glossaries are buried in unintuitive tools, business teams quickly fall back into old habits: personal Excel files, duplicated queries, and working with data without fully understanding its context. This is a barrier we can actually lower today. With generative AI tools – like large language models (LLMs) – it’s possible to create a simple, natural interface. One where a user can ask, “How is gross revenue calculated?” and get a clear, contextual answer, based on up-to-date metadata and glossaries. 

What to do instead 
Focus on the end-user experience. Instead of expecting people to dig through complex tools and interpret technical documentation, give them a simple, intuitive way to interact with data. Natural language interfaces – powered by LLMs – allow users to ask questions in plain language and receive clear, contextual answers. This approach not only makes data knowledge more accessible, but also encourages real, everyday use. And that’s the goal: for data to be available, understandable, and actually used. 

Watch the webinar recording: “LLMs in Data Governance” 

As you’ve seen, simply completing a Data Governance project doesn’t automatically solve all data issues in an organization. But that doesn’t mean such initiatives are doomed to fail. When the challenges above are addressed, Data Governance can become a well-structured process that truly supports decision-making, organizes data, and builds trust in information. A modern approach – powered by large language models (LLMs) – makes knowledge more accessible, automates documentation, and increases data adoption in everyday business operations. 

Watch the recording of our webinar: click here.

Let’s talk 

If these challenges sound familiar and you’re looking for ways to address them in your organization – let’s connect. We’re currently offering free consultations, and we’d be happy to talk through your needs. 

Book a free session with one of our experts: click here.