Term

DataOps

DataOps lifts the DevOps playbook (CI, automated testing, version control, observability, small reversible changes) and applies it to the data-pipeline lifecycle. It is the cousin of MLOps, but where MLOps governs model artefacts, DataOps governs the analytical data flowing in and out of them. The discipline is mostly about how teams work, the tooling sits underneath.
Reviewed by 7wData

Why it matters

Without DataOps, data pipelines are the single biggest source of analytical-trust decay. Schema drift goes silent, an upstream field type changes and nothing catches it, freshness alarms arrive after the Monday dashboard has already been read. For AI this is not a side issue. Training data quality is a direct function of pipeline rigor, and ungoverned pipelines produce ungoverned models. I am seeing teams invest heavily in model evaluation while leaving the data layer on trust, which is the inversion of where the risk actually lives.

Where you’ll encounter it

Three contexts. First, a vendor pitches a “DataOps platform”, and on inspection it is a CI/CD tool with a few dashboards bolted on. Second, an engineering retrospective traces a quarter-end reporting embarrassment back to pipeline drift that nobody owned end to end. Third, a model-risk review walks back from a stale prediction to a feature pipeline that quietly stopped refreshing weeks earlier. The same lesson surfaces in all three: DataOps is org practice first, tooling second. Buying the platform without changing the working agreements is the most expensive way to learn this.

Part of the 7wData AI Glossary. Tracking how concepts like this move in the expert conversation: daily signals at ins7ghts.com.