Can you connect data governance practice to regulatory obligation? This tests whether you understand how EU AI Act documentation requirements map to governance program design — not just whether you know the regulation exists.

1. Data provenance documentation — establish end-to-end lineage from source to model input using OpenLineage or equivalent; document data origin, consent basis, and transformation history. 2. Data quality standards — define DQ rules appropriate for the AI use case, implement automated quality gates using tools like Great Expectations, and track DQ KPIs. 3. Training data labeling governance — implement labeling quality assurance programs; track annotator agreement; audit for bias in labeled datasets. 4. Audit trail maintenance — ISO/IEC 42001 and EU AI Act both require documented evidence of governance processes; build catalog-based documentation workflows. 5. Bias auditing — assess training dataset representativeness across demographic categories; flag and remediate underrepresentation before model training. (Source: EU AI Act, NIST AI RMF, ISO/IEC 42001:2023)

This is the foundational framework question. Interviewers for senior data governance roles expect deep familiarity with DMBOK — not just awareness that it exists.

DAMA-DMBOK v2 is the Data Management Body of Knowledge, covering 14 knowledge areas: Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, Data Quality, Big Data and Data Science, Data Management Maturity, and Data Management Organization and Role Expectations. For AI governance specifically, the most relevant areas are: Data Quality (DQ rules, profiling, monitoring), Metadata Management (lineage, business glossary, data catalog), Data Governance (policies, stewardship, accountability), and Data Security (access control, classification, privacy compliance). DMBOK 3.0 global launch event was June 25, 2025. (Source: DAMA International, vendor-reported)

This is the real-world challenge of the role. Technical governance frameworks are the easy part — organizational adoption is what separates successful programs from shelfware.

Sustained adoption requires five elements: 1. Executive sponsorship — governance programs without C-suite commitment fail; identify your executive champion (CDO or CTO) before launching. 2. Business value framing — translate governance outcomes into business terms: reduced data incident costs, faster ML deployment cycles, regulatory risk reduction. 3. Federated stewardship model — embed data stewards within business domains rather than centralizing all governance in a COE; accountability follows ownership. 4. Incremental wins — demonstrate value early through a high-visibility data quality improvement or a specific compliance deliverable (GDPR records of processing). 5. Tooling usability — governance platforms only succeed if business users can operate them without IT mediation. Collibra and Alation are built for business user adoption; configure for your stakeholders.

This tests depth in data governance frameworks. DCAM is the financial services standard — if you’re interviewing at a bank, asset manager, or insurance firm, they may use DCAM as their primary governance reference.

DCAM (Data Capabilities Assessment Model) v3, from the EDM Council, is an industry standard with 8 core components, 35 capabilities, and 109 sub-capabilities for data management in financial services. DCAM v3 includes enhanced AI/ML integration, making it directly relevant for AI data governance. DAMA-DMBOK is the broader, industry-agnostic framework (14 knowledge areas) — applicable across industries. DCAM is the financial services specialization: it is organized around demonstrable capabilities rather than knowledge areas, making it more directly assessable. When recommending: use DMBOK when your organization needs a comprehensive foundational framework applicable to all business domains; use DCAM when operating in financial services, insurance, or another DCAM-adopting sector, or when regulatory alignment (BCBS 239, DORA) is a primary driver. Both frameworks are increasingly relevant for AI governance given the EU AI Act’s data documentation requirements. (Source: EDM Council, vendor-reported)

Synthetic data governance is an emerging and increasingly important topic as organizations use AI-generated datasets to augment training data or protect privacy. This tests whether you have current knowledge.

Synthetic data governance requires a purpose-built framework layered on top of traditional data governance: 1. Provenance documentation — document the generative method, the seed dataset, and any filtering or transformation applied; this is the lineage chain for synthetic data. 2. Bias propagation auditing — synthetic data generated from biased seed datasets will propagate and potentially amplify those biases; audit seed data before generation and validate outputs for representativeness. 3. Fidelity and utility assessment — verify that synthetic data maintains the statistical properties required for model training; fidelity metrics (distributional similarity) confirm the data is fit for purpose. 4. Privacy risk assessment — even “synthetic” data can leak information about individuals in the seed dataset; conduct membership inference testing and apply k-anonymity or differential privacy where required. 5. Regulatory classification — EU AI Act and GDPR have specific positions on synthetic data; document the regulatory basis for use. (Source: role-post-data-governance-manager-ai.md)