Data science maturity: stages, assessment, and roadmap

Data science maturity is defined not only by an organization’s ability to build and deploy models, but by whether stakeholders can trust, verify, and consistently use their outputs. The article argues that many organizations have sophisticated technical capabilities but lack shared metric definitions, clear ownership, lineage, access controls, and documentation, creating risks such as conflicting dashboards, shadow models, and unreliable AI-generated answers. It recommends assessing maturity through prediction access, metric consistency, AI-output explainability, and decision coverage, emphasizing that the proportion of important decisions informed by trusted analysis matters more than model counts. A progressive governance roadmap begins with documenting and endorsing important data assets, then adds centralized metrics, role-based access, version-controlled change policies, and eventually expands to semantic layers, lineage, observability, and auditable governance. The article presents governance as an enabler of faster, safer self-service analytics and positions Hex as a platform for providing governed context to AI-assisted data exploration.

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