Data-as-a-Service: Why Data Platforms Should Be Run, Not Just Built

A common pattern in data platform projects: a team builds the pipelines, hands over documentation, and moves on. Six months later, schemas have drifted, a source system changed its API without anyone noticing, and the 'single source of truth' has quietly become one of several.

Data platforms are operational systems

Treating a data platform as a one-time deliverable ignores that data sources, business definitions and downstream consumers keep changing after launch. A platform that isn't actively operated will drift out of sync with the business it's meant to serve.

• Monitoring that catches schema drift and pipeline failures before analysts do
• A standing process for evolving the semantic layer as the business changes
• Clear ownership for data quality issues, not a shared responsibility no one owns
• Capacity planning as data volume and usage grow

This is the thinking behind running data platforms as a continuous, Data-as-a-Service capability — not a finished project, but a system that's maintained with the same discipline as any other production infrastructure.