AITM-DR — AI Data Readiness Associate

What Data Readiness Is (and What It Is Not)

Data Quality Dimensions Extended for AI

Data Governance and Data Contracts

Data Lineage, Provenance, and Documentation

Labeling Strategy and Annotation Governance

Feature Stores and Vector Stores as Governance Artifacts

Bias-Relevant Variables and Subgroup Coverage

Privacy, Sensitive Data Classes, and Data Minimization

Third-Party and Open-Source Data Readiness

Drift Monitoring, Incident Classification, and Sustainment

The Readiness Scorecard

Lab 1 — Dataset Profiling and Quality Scoring

Lab 2 — Data Contract and Datasheet for a RAG Source

Case Study — Amsterdam SyRI and Rotterdam Welfare-Fraud Algorithm

Template — AI Data Readiness Scorecard