VentureBeat/Ideogram

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 tim...