/cs:cdo-review — CDO Forcing Questions

/cs:cdo-review <plan>

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-review

  — 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)