The Data Looks Healthy. But the Culture is Cracking.
A Data Culture Lens
The charts say we’re fine.
The GitLab dashboards light up with encouraging trends , merge request throughput is on the rise, lead times are shrinking, and deployment frequency is solid. Over in Jira, cycle times look healthy, story points are steadily burning down, and the velocity chart shows all the right curves. From a tooling perspective, everything seems to be working. Work is moving, issues are closing, and the numbers tell a reassuring story.
But the people, the ones behind the commits, behind the tickets, are telling another story. Quietly. Often off-record. Sometimes not at all, unless you’re paying close attention.
“I don’t know why we’re building this.”
“I’m not sure it even matters.”
“I just do what’s on the board.”
These aren’t data points. They’re sentiments. But they speak volumes. And the silence that often follows them? That’s even louder.
In data cultures, we’re wired to chase visibility. We seek patterns, dashboards, clarity. We build systems that optimize for what can be measured. And in software development, that has meant creating well-instrumented environments where delivery can be observed in near real time. But in this rush to quantify delivery, we’ve left something behind: the human signal.
We know when a commit lands. We know how long a merge request took. We know the cycle time between “To Do” and “Done.” But we rarely know if the people doing the work feel connected to it. We don’t track belief. Or doubt. Or purpose. We don’t build observability for misalignment. And we certainly don’t make space for it in our dashboards.
And yet, those are the things that break teams long before the numbers ever do.
Healthy metrics can mask an unhealthy culture. A team might be shipping fast but feeling directionless. They may hit their delivery targets while quietly disengaging. They may be productive, but not fulfilled. This is the cultural debt we accumulate when we confuse visibility with understanding.
As data practitioners, we have to expand our lens. What if we treated dashboards as conversation starters rather than conclusions? What if we combined the quantitative clarity of our tooling with qualitative pulse checks like casual, frequent, honest windows into the emotional state of the team?
This isn’t about softening the science of delivery—it’s about making space for the art of working well together. It’s about asking what our metrics don’t tell us. It’s about creating a culture where engineers feel seen not just as output generators, but as co-creators of value.
When we prioritize data culture, we must include the human layer. Because data without trust is just noise. And dashboards without dialogue are dangerous.
If we want our data practices to truly empower teams, then we need systems that see not just how work moves, but how people feel while moving it.
Curious to know how this plays out in the trenches of engineering leadership? Read the perspective on Data, Chai & Dialogue
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