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In the past decade, companies have spent billions on data infrastructure. Petabyte-scale warehouses. Real-time pipelines. Machine learning (ML) platforms.
And yet — ask your operations lead why churn increased last week, and you’ll likely get three conflicting dashboards. Ask finance to reconcile performance across attribution systems, and you’ll hear, “It depends on who you ask.”
In a world drowning in dashboards, one truth keeps surfacing: Data isn’t the problem — product thinking is.
The quiet collapse of “data-as-a-service”
For years, data teams operated like internal consultancies — reactive, ticket-based, hero-driven. This “data-as-a-service” (DaaS) model was fine when data requests were small and stakes were low. But as companies became “data-driven,” this model fractured under the weight of its own success.
Take Airbnb. Before the launch of its metrics platform, product, finance and ops teams pulled their own versions of metrics like:
Nights booked
Active user
Available listing
Even simple KPIs varied by filters, sources and who was asking. In leadership reviews, different teams presented different numbers — resulting in arguments over whose metric was “correct” rather than what action to take.
These aren’t technology failures. They’re product failures.
The consequences
Data distrust: Analysts are second-guessed. Dashboards are abandoned.
Human routers: Data scientists spend more time explaining discrepancies than generating insights.
Redundant pipelines: Engineers rebuild similar datasets across teams.
Decision drag: Leaders delay or ignore action due to inconsistent inputs.
Because data trust is a product problem, not a technical one
Most data leaders think they have a data quality issue. But look closer, and you’ll find a data trust issue:
Your experimentation platform says a feature hurts retention — but product leaders don’t believe it.
Ops sees a dashboard that contradicts their lived experience.
Two teams use the same metric name, but different logic.
The pipelines are working. The SQL is sound. But no one trusts the outputs.
This is a product failure, not an engineering one. Because the systems weren’t designed for usability, interpretability or decision-making.
Enter: The data product manager
A new role has emerged across top companies — the data product manager (DPM). Unlike generalist PMs, DPMs operate across brittle, invisible, cross-f …