CDO Review

# /cs:cdo-review — CDO Forcing Questions **Command:** `/cs:cdo-review <plan>` 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. ## When to Run - 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" ## The Six CDO Questions ### 1. What decision does this data drive? **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. ### 2. What's the consent provenance for every source? **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 `ai_training_data_audit.py` if there's any AI use case in scope. ### 3. Who consumes this internally — and how many distinct functional domains? **Drives the centralize-vs-embed and warehouse-vs-mesh decisions.** - <5 consumers: warehouse-only. - 5-25 consumers: lakehouse. - 25+ consumers + federated culture: mesh. - Premature architecture choice is the #1 cause of data-team burnout. ### 4. What's the M&A diligence impact? **If an acquirer asks about this data corpus tomorrow, are we ready?** - Is there a documented anonymization process? - What % of customers have MSA carve-outs? - Are training-data provenance logs current? - Run `data_asset_valuator.py` quarterly. ### 5. Can the model / decision / report be retrained / re-run / re-published without this source? **Tests how much you depend on a specific data source.** - If yes → low blast radius; you can change consent posture later. - If no → high blast radius; you've structurally committed to the source. Vet harder. ### 6. What role unblocks this — and is it the right next hire? **Wrong hire (data scientist) when right answer (analytics engineer) is a 12-month productivity loss.** - Map the decision being unblocked to the specific role. - Confirm prerequisite roles are in place (data engineer before ML engineer, analyst before data scientist). ## Workflow ```bash # 1. AI training audit (if any ML / AI use case) python ../../../skills/chief-data-officer-advisor/scripts/ai_training_data_audit.py sources.json # 2. Architecture decision (if changing the stack) python ../../../skills/chief-data-officer-advisor/scripts/data_product_strategy_picker.py profile.json # 3. Data asset valuation (if productizing or pre-M&A) python ../../../skills/chief-data-officer-advisor/scripts/data_asset_valuator.py corpus.json ``` ## Output Format ```markdown # CDO Review: <plan> **Date:** YYYY-MM-DD ## The Decision Being Made [one sentence — which of the four CDO decisions: training | architecture | asset | hire] ## Training Audit (if applicable) - NO-GO sources: N - MITIGATE sources: N - GO sources: N - Top remediation: <one line> ## Architecture (if applicable) - Recommended: WAREHOUSE / LAKEHOUSE / MESH - Build-vs-buy summary: <one line> - Kill criteria: <when to revisit> ## Asset Value (if applicable) - Strategic value: X/10 | Moat: STRONG / MEDIUM / WEAK - M&A multiplier: X.Xx – X.Xx ARR - Recommended productization path: <name> ## Org (if applicable) - Next hire: <role> - Why this, not that: <one line> - Prerequisite hires in place: yes/no ## Verdict 🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK ## Next Steps [3 concrete actions] ``` ## Routing - `/cs:gc-review` — for any productization or licensing path - `/cs:ciso-review` — for any architecture change touching customer data - `/cs:cfo-review` — for build-vs-buy TCO and M&A valuation math - `cs-chro-advisor` agent — for data team hires (comp, ladder, leveling) - `/cs:decide` — log the verdict - `/cs:freeze 90` — on multi-year infrastructure contracts ## Related - Agent: [`cs-cdo-advisor`](../../agents/cs-cdo-advisor.md) - Skill: [`chief-data-officer-advisor`](../../../skills/chief-data-officer-advisor/SKILL.md) - Adjacent: `../../../skills/general-counsel-advisor/` (contractual constraints), `../../../skills/cto-advisor/` (architecture capacity) --- **Version:** 1.0.0