DataGov 2.0 represents an evolution of traditional data governance, integrating Model Governance, AI Risk Management, and a semantic layer to handle AI assets. These assets include models, prompts, AI agents, RAG corpora, embeddings, and vector indexes, all requiring lifecycle management and access control.
Companies must audit their AI landscape by analyzing infrastructure, financial records from AI providers, security logs, and internal team processes. Establishing a centralized registry allows organizations to track AI use cases, assign ownership, and monitor performance metrics effectively.
Controlling costs is critical, as AI expenses extend beyond tokens to include search, embeddings, tool usage, and human oversight. A comprehensive dashboard should track total request costs and operational metrics to identify inefficiencies or model hallucinations early.
Ensuring AI quality requires evaluating RAG systems at every stage, including document retrieval, search accuracy, and response generation. Organizations should utilize AI-ready data, which features clear business definitions, machine-readable schemas, and established quality checks.
Implementation follows a phased approach: first, auditing existing tools and data readiness; second, defining control rules and security checklists; and finally, automating quality checks within the corporate platform.
