Overview

The six foundational components of AI data readiness. Each subpage covers the full framework, AI-specific failure modes, tooling landscape, and a practical readiness checklist.

Read in order if you are new to the topic. Jump to a specific component if you are targeting a known gap.

#	Component	What it covers
01	Data Quality	Six quality dimensions, four AI failure modes, tooling, checklist
02	Data Governance	Ownership, bias monitoring, explainability, NIST / EU AI Act / ISO 42001
03	Access & Integration	Silos, four integration patterns, lakehouse / fabric / mesh, agentic access
04	Lineage & Metadata	Four lineage types, active metadata, business glossary, model lineage
05	Infrastructure Readiness	MLOps, LLMOps, AgentOps, seven infrastructure layers, maturity ladder
06	Security & Compliance	Six AI threat categories, privacy law, access control, audit trails