この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。

/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

# 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

# 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-review — for data team hires (comp, ladder, leveling)
/cs:decide — log the verdict
/cs:freeze 90 — on multi-year infrastructure contracts

Agent: cs-cdo-advisor
Skill: chief-data-officer-advisor
Adjacent: ../../../skills/general-counsel-advisor/ (contractual constraints), ../../../skills/cto-advisor/ (architecture capacity)

Version: 1.0.0

cdo-review

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