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?

• "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?

• 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?

• <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?

• 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?

• 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?

• 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-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

• Skill: chief-data-officer-advisor

• Adjacent: ../../../skills/general-counsel-advisor/ (contractual constraints), ../../../skills/cto-advisor/ (architecture capacity)

---

Version: 1.0.0