7 Data Governance myths debunked
#13 Or how to separate fact from fiction
Hello 😀
Data governance is surrounded by myths primarily because it's often misunderstood or perceived as overly complex. It’s also confused with data management… 😅
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Agenda
What’s up with myths
The 7 myths debunked
How to react
What’s up with myths
During the very first month that I work for a new company, I probably spend half of my time just debunking their beliefs on Data Governance.
And this happens every time.
I'm not sure why data governance has such bad press, but that’s the reality.
Here’s what I usually hear :
“ All these roles are scary and are gonna give us additional workload ”
“ It’s really blurry, what are the deliverables anyway ”
“ A data catalog would solve everything but is super expensive ”
“ I hope you can solve all our data issues ”
🤯 🤯 🤯
🔍 Tip : Breath, it’s normal. It takes time to take action, prove the value and in the end change people’s minds.
The 7 myths debunked
Myth 1 : Data Governance is just for big companies
👉 Reality : While large organizations have more complex data ecosystems, data governance is essential for businesses of all sizes.
When a startup grows above 10 employees, it needs clear policies around data ownership, quality, and usage to avoid chaos.
Here’s what they usually encounter :
Data duplication
Since more people mean the creation of silos, some data will be either created or collected twice. It makes it hard to understand which one is the source of truth.
Data ownership confusion
Data is edited by many but owned by none.
Myth 2 : Data Governance is IT’s responsibility
👉 Reality : While IT plays a crucial role, data governance is a cross-functional effort. It requires collaboration between IT, data teams, and business units.
Business users understand the context of data, while IT ensures the infrastructure and tools to support governance. Effective governance hinges on shared responsibility.
🤯 Yet, historically Data management and Master data teams are on IT side.
I guess that’s why there’s always been this confusion on data governance responsibility.
Myth 3 : Data Governance slows down innovation
👉 Reality : Good data governance actually speeds up innovation by ensuring data is accurate, accessible, and trusted.
Data teams will never be able to experiment if they don’t have the right data, or at least some extracts of it to test.
That’s why companies with strong documentation will have a big competitive advantage thanks to AI assistants.
Good documentation means having text information that is :
• Accurate and up-to-date information
• Consistent structure and format
• Detailed and contextual, step-by-step instructions
• Accessible and searchable
🔍 Tip : Dropping PPT is probably the best idea right now if you want to implement AI assistants in the future.
Myth 4 : Data Governance is a one-time project
👉 Reality : Data governance is not a "set it and forget it" initiative.
New data sources, regulations, and technologies constantly emerge, requiring updates to your governance policies.
A couple years ago we were not talking about data contracts or data observability. Methods and technologies for data governance are evolving over time.
Data Governance is an ongoing process that evolves with your business.
Myth 5 : Data Governance requires expensive tools
👉 Reality : While some companies invest in complex data governance platforms, you don’t need expensive tools to get started.
I’ve shared in my last post my Starter Kit, showing that you can do things with low resources.
🤓 You can begin with simple solutions like spreadsheets or leveraging existing tools like SharePoint, Google Drive or even project management tools like Notion to track data ownership, quality, and usage.
Myth 6: Data Governance is too complicated to implement
👉 Reality : While data governance can seem daunting, you don’t need to implement everything at once.
Start with the smallest scope possible.
Identify the most critical data elements on which people are complaining: they might be fighting over the calculation method, having a hard time finding or getting the data. And this has an impact on the organization.
💼 Begin by :
defining simple ownership rules,
creating a repository with metrics definitions,
and establishing basic data quality checks.
Even incremental efforts deliver measurable results.
Myth 7: Data Governance solves all data problems instantly
👉 Reality : Data governance isn’t a magic bullet. It doesn’t fix poor data quality overnight or solve data silos immediately.
Be careful with people who think that you will solve everything for them. They need to understand that they are part of the solution !
Data Governance is here to provide them the framework and processes to identify, address, and prevent their data issues over time.
Overall it’s about building long-term discipline for managing data better.
How to react
Lately I’ve started to state very clearly what Data Governance is NOT.
I’ve found it super useful to explain it at the very beginning of meetings on Data Governance to raise awareness.
It’s also a good way to avoid misunderstandings with IT people…
You can use this example above for your presentations.
See you soon,
Charlotte
I'm Charlotte Ledoux, freelance in Data & AI Governance.
You can follow me on Linkedin !