Agent skill · product strategy · alirezarezvani
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.
Why this skill is useful
Adds specialized scripts for auditing AI training data eligibility and valuing customer data that are not commonly found in public resources.
What it needs
Requires python installed locally. About 6k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
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 Key Questions (ask these first) What decision does this data drive? (If none, why are we collecting it?) What's the consent provenance of every source we want to train on? (TOS-only is not the same as explicit opt-in.) Who are the internal data consumers, and how many distinct domains do they span? (Drives centralize-vs-embed and warehouse-vs-mesh.) In an M&A scenario, is our data a moat or a liability? (Customer carve-outs in MSAs can flip the answer.) Are we hiring an analytics engineer or a data scientist next? (They solve differ …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/chief-data-officer-advisor--a3314d