The product provides a shared control plane where teams define data product requirements, source-of-truth ownership, quality expectations, model training data needs, and experiment metrics. It then syncs with the data stack to monitor pipeline availability, validate datasets, document metric definitions, and surface readiness status for analytics, experimentation, and predictive modeling use cases.

Companies are hiring across data science, analytics engineering, and data engineering to make data usable for product analytics, ML, and experimentation. AI-assisted workflows are also becoming accepted inside analytics teams, creating demand for tools that automate data validation, documentation, and coordination work.