Chief Data Officer Advisor
Use for data strategy decisions: training models on customer data, warehouse vs...
Install
npx skills add alirezarezvani/claude-skills --skill chief-data-officer-advisor -g -a claude-code -y- Repository
- alirezarezvani/claude-skills29.1k
- Category
- Engineering
- Updated
- 4d ago
- License
- MIT
- Context cost
- Mediumabout 2,647 tokens
- Runs scripts
- Yes, review before use
What it does
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.
Chief Data Officer Advisor
Strategic data leadership for startup CDOs and founders without one. Four decisions, no surveys:
- Can we train our model on this data?, origin × consent × use-case matrix
- Warehouse, lakehouse, or mesh, and what do we build vs buy?, stage-driven architecture
- What is our customer data worth?, strategic value + M&A multiplier + productization paths
- 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
python scripts/ai_training_data_audit.py # uses embedded sample
python scripts/ai_training_data_audit.py path/to/sources.json
# Pick data architecture + build-vs-buy + sequencing
python scripts/data_product_strategy_picker.py # uses embedded Series A SaaS
python scripts/data_product_strategy_picker.py path/to/profile.json
