Individual skill

chief-data-officer-advisor

Use for data strategy decisions: training models on customer data, warehouse vs lakehouse vs mesh, valuing data for a raise or M&A, or data hiring order. Runs ai_training_data_audit.py, data_product_strategy_picker.py and data_asset_valuator.py.

c-level-advisor/chief-data-officer-advisor/skills/chief-data-officer-advisor/SKILL.md

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Repository stars (not skill ratings): 29,605
Repository last updated: Nov 5, 2026
License: MIT

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SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
SKILL.mdcore · 11,328 bytes
references/ai_training_data_rights.mddoc · 7,563 bytes
references/customer_data_as_asset.mddoc · 10,285 bytes
references/data_product_strategy.mddoc · 8,612 bytes
references/data_team_org_evolution.mddoc · 10,446 bytes
scripts/ai_training_data_audit.pyruns · 19,434 bytes
scripts/data_asset_valuator.pyruns · 14,664 bytes
scripts/data_product_strategy_picker.pyruns · 14,828 bytes

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# 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