You’ve probably felt the tension.

A product team ships tracking for a new feature. Data engineers tweak the pipeline. Analysts wait for numbers they can trust. Somewhere along the way, communication breaks down:

[[calloutBlock=

“Why didn’t we know this event structure changed?”
“How much data did we lose?”

]]

Ownership was murky—and the damage is clear.

I’ve spent years fixing messy data—first as a data scientist, now building tools at Avo. Again and again, I’ve seen one silent culprit causing bloated tracking plans, broken metrics, and endless back-and-forth: unclear ownership.

The Governance Gap

As companies grow, good data becomes a coordination problem, not a tooling one. Company-wide analysis and data activation depend on consistent data, yet day-to-day work happens in silos. When a change lands, nobody is sure who must sign off, Slack threads explode, and progress stalls.

Our Data Mesh for Event-Based Data whitepaper calls this the governance dilemma: move fast and risk chaos, or slow down to guard quality. Most orgs want a third path – the ability to transfer ownership to the people doing the work without losing control – but they lack a concrete framework.

Two perspectives sum up the dilemma:

• Product teams need early visibility into changes—and a chance to weigh in on data structures they both consume and implement.
• Central data teams want product teams to own their tracking, yet must protect business-critical events.
  

Without a clear system to define and enforce who owns what – and facilitate cross-functional collaboration – work gets blocked, changes slip through the cracks, and central reviewers turn into bottlenecks.

The Ownership Gap

Across dozens of conversations with Avo customers, one theme is constant: when an event changes, teams need instant clarity: who relies on it and who must sign off.

But in most organizations, ownership isn’t operational. It hides in Confluence pages, tribal memory, or nowhere at all, so every schema tweak becomes detective work:

• Approval requests bounce around in DMs.
• The same questions—Who owns this? Who reviews it?—surface on every branch.
• Central data teams burn cycles triaging changes instead of building data products.
  

Taking ownership from theory to practice

The people closest to the product have the subject-matter expertise—so they’re uniquely qualified to own the data that measures its success. In Data Mesh, this principle is called “domain-driven ownership.”

For it to work, ownership must be:

• Explicit – Anyone in the org should be able to see who owns what.
  
• Enforceable – Owners should be notified and required to review relevant changes.
  
• Contextual – Not every change needs every stakeholder’s review, but the right ones should trigger it automatically.
  

Over the past year, the Avo team has been focused on bridging this gap, providing effective solutions to put ownership into practice. 

As one customer put it:

I need to find a way to assign ownership to the different areas in order to effectively get people to pay attention and to fix it.

These aren't just feature requests. They’re signs of a deeper need: operationalized ownership that scales with how teams actually work.

What’s Next

If you haven’t read it, grab our whitepaper for the full recipe on moving from chaos to clarity: 

Download the Data Mesh whitepaper →

In our next post, we’ll show how Avo now turns those principles into practice—automating reviews, surfacing ownership, and making governance scalable instead of painful.

Until then, we’d love your thoughts: “What’s the biggest ownership headache you face today?" Let us know.