Chief Data Officer Advisor

Strategic data leadership for startup CDOs and founders without one. Four decisions, no surveys:

1. Can we train our model on this data? — origin × consent × use-case matrix
2. Warehouse, lakehouse, or mesh — and what do we build vs buy? — stage-driven architecture
3. What is our customer data worth? — strategic value + M&A multiplier + productization paths
4. What data role do we hire next? — stage-to-role map, centralize-vs-embed trigger

This skill does not cover tactical data engineering. For schema design, observability, query optimization, RAG, or ML platform implementation, see engineering/database-designer/, engineering/observability-designer/, engineering/data-quality-auditor/, engineering/sql-database-assistant/, engineering/rag-architect/, engineering/llm-cost-optimizer/.

Keywords

CDO, chief data officer, AI training data, consent provenance, training rights, GDPR Article 6 lawful basis, GDPR Article 22, EU AI Act high-risk, ePrivacy, copyright fair use, hiQ v. LinkedIn, scraped data, synthetic data, data product, data mesh, lakehouse, medallion architecture, dbt, Snowflake, BigQuery, Databricks, Fivetran, Airbyte, reverse ETL, feature store, customer data as asset, data monetization, data productization, anonymization, k-anonymity, differential privacy, M&A data diligence, data org, analytics engineer, data engineer, data scientist, data product manager, centralize vs embed, hub and spoke

Quick Start

# Audit data sources for AI training eligibility