Audit training data origins and design data products

Audit AI training data, pick data product strategy, value assets

Chief Data Officer or CPO deciding on data moat strategy, training data sourcing, and infrastructure architecture when legal + product + financial trade-offs collide.

• · Data sources (origin, class, use-case, license)
• · Current data infrastructure (warehouse vs lakehouse)
• · Customer data assets (value, carve-out risk)

• · AI training data audit (origin x class x use matrix → GO/MITIGATE/NO-GO per GDPR Art. 6 + EU AI Act)
• · Data product strategy (warehouse vs lakehouse vs mesh, 6-layer build-vs-buy, 12-month sequencing)
• · Data asset valuator (strategic 0-10 score, M&A multiplier, productization paths)

• · None (stdlib-only)

• · chief-data-officer-advisor skill installed or c-level-skills
• · Clear data sources and use-cases documented
• · Known customer data assets (PII, behavioral, preference)

• · AI training rights unclear (GDPR Art. 6 ambiguous in domain)
• · Data product strategy doesn't account for real-time requirements
• · Asset valuation ignores M&A due diligence risk
• · Regulatory landscape changes (EU AI Act loopholes close)

• · GDPR Art. 6 + EU AI Act citations with GO/MITIGATE/NO-GO framework
• · 3 ranked productization paths per data asset
• · 4 references citing 5+ authoritative sources each