Data governance definition and business value, DAMA-DMBOK framework domains, Data governance maturity models (Gartner, IBM, EDM Council), Roles and responsibilities (data stewards, custodians, owners), Data governance...
Data governance definition and business value, DAMA-DMBOK framework domains, Data governance maturity models (Gartner, IBM, EDM Council), Roles and responsibilities (data stewards, custodians, owners), Data governance council structure, Policy development and enforcement, Data classification schemes and labeling strategies.
Data quality dimensions (accuracy, completeness, consistency, timeliness, validity, uniqueness), Data profiling techniques and anomaly detection, Data quality rules engines, Master Data Management (MDM) strategies, Data lineage capture (technical, business, operational), Columnar lineage and impact analysis, Data cataloging and metadata management.
Privacy by design principles (proactive, embedded, data minimization), GDPR principles and data subject rights, CCPA/CPRA requirements, Privacy Impact Assessments (PIA/DPIA), Data Protection Officers responsibilities, Cross-border data transfers (SCCs, BCRs), Privacy seals and certifications (ISO 27701, TrustArc).
k-Anonymity, l-Diversity, t-Closeness, Differential privacy fundamentals ( , privacy budgets), Local vs. central differential privacy, Synthetic data generation (CTGAN, TVAE), Homomorphic encryption use cases, Secure Multi-Party Computation (SMPC), Federated learning privacy guarantees.
Algorithmic bias sources and amplification, Fairness metrics (demographic parity, equal opportunity), Group vs. individual fairness, Counterfactual fairness, Intersectional discrimination, Explainable AI for regulatory compliance, AI ethics frameworks (IEEE, Partnership on AI), Algorithmic accountability and audit trails, Data ethics review boards.