TL;DR
Data products fail when teams ship outputs without owning the underlying platform that sustains them over time.
TL;DR
Data products fail when teams ship outputs without owning the underlying platform that sustains them over time.
2026-09-07 - Data Products, Platform Ownership, Enterprise Data
There is a familiar cycle playing out across enterprise data teams right now. Organizations adopt the language of data products. They assign domain teams to package up tables, metrics, and streams for internal consumers. Everyone gets excited about treating data as an asset. Then, six months later, the product breaks, the original creator moves to a different project, and nobody knows who is supposed to fix it.
I have watched this happen from inside the room more times than I can count. The strategy sounds appealing on paper. Give business domains autonomy, let them build their own data products, and watch innovation scale. But autonomy without platform ownership is just organized chaos.
When people talk about data products, they usually focus on the interface—the schema, the documentation, the dashboard, or the API endpoint. They treat the data product like a standalone application. But data products do not live in a vacuum. They rely on ingestion pipelines, transformation logic, storage layers, and orchestration tools that span the entire enterprise ecosystem.
If you tell a domain team to build a data product without giving them a dedicated platform team to own the underlying pipes, you are asking them to do two full-time jobs at once. They have to understand business logic and manage infrastructure reliability. Predictably, they end up doing neither well.
Platforms are owned and maintained, not shipped and abandoned. When an enterprise treats a data platform as a project—something you deliver, celebrate, and hand off to operations—maintenance becomes nobody's problem. Over a career spanning more than a decade in this space, I have learned a simple rule: if a system does not have an explicit, named owner responsible for its health next Tuesday, next quarter, and next year, it is already decaying.
A common mistake in decentralizing data ownership is stripping away centralized controls in the name of speed. Organizations loosen standards so domain teams can move fast and ship products quickly.
This approach runs backward. Controls must come first. Automation built on ungoverned data just helps bad architecture fail faster. When you let every domain define its own standards for data quality, lineage, and access control, you end up with a collection of silos that cannot talk to each other. Consumers lose trust, executives stop relying on the metrics, and the whole initiative stalls.
True scale requires guardrails that make the right way to build a data product also the easiest way. A solid platform operating model defines clear boundaries:
Systems should be built to run without depending on any single heroic individual. Too often, a data product works brilliantly while its creator is on the team, but becomes unmaintainable the moment they leave. That is key-person dependency disguised as agility.
When we design enterprise platforms, our goal should be boredom. We want infrastructure that runs quietly in the background, data products that behave predictably, and governance models that prevent silent failures before they reach executive reporting.
Treating data as an enterprise asset requires the same balance sheet rigor you would apply to any other core infrastructure. That means investing in the platform layer first, defining clear ownership before writing code, and recognizing that a data product is only as reliable as the foundation it sits on.