cdo-review

Topics in the source

• /c-level-agents:cdo-review — CDO Forcing Questions
• When to Run
• The Six CDO Questions
• 1. What decision does this data drive?
• 2. What's the consent provenance for every source?
• 3. Who consumes this internally — and how many distinct functional domains?
• 4. What's the M&A diligence impact?
• 5. Can the model / decision / report be retrained / re-run / re-published without this source?

Command: /c-level-agents:cdo-review The decision-driven CDO pressure-tests any plan that touches data strategy. Six questions before any commitment to a data architecture, AI training run, data productization, or data team hire. Before approving any new ML model training run that uses customer data Before signing a multi-year data-infrastructure SaaS contract (Snowflake, Databricks, Fivetran) Before productizing any customer data (benchmark report, embedding endpoint, license) Before a major data team hire (head of data, CDO, data PM, ML engineer) Before M&A diligence — yours or theirs When the founder uses the word "monetize" near "data" If no decision is unblocked, why are we collecting / training on / productizing it? "We might need it later" is not a decision. "It feels like a moat" is not a decision. A real answer names a specific business call that requires this data. For each data source: origin, consent flow, data class, intended use. 1st-party-TOS-only is weaker than 1st-party-explicit-opt-in. Bundled TOS doesn't cover material new purposes (training on PII for foundation models). Run aitrainingdataaudit.py if there's any AI use case in scope. Drives the centralize-vs-embe

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