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Chief Data Officer Plan Review

Review a data plan rights-first: training and resale rights, privacy and deletion, data quality and lineage, and a proceed or stop verdict.

Research & Data / Researchdata-governancedata-privacy
by AgentVoltv1.0.0
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Before & after

Same prompt, same agent — the only difference is the skill.

Prompt“Validate our plan to productize customer data for recurring revenue”
Beforegeneric AI agent
That's a significant decision that touches on data strategy, legal, and product direction. Before moving forward, you'd want to think about several things: First, do you have the contractual right to productize customer data? You'll need to check your customer agreements to see if you explicitly have permission. Second, what data protection and privacy regulations apply—GDPR, CCPA, others? Third, what's the technical architecture for keeping customer data separate from your productized data? And fourth, what's your competitive advantage in offering this data product versus competitors?

I'd suggest starting with a legal review and a technical architecture review before you commit resources. Have you consulted with a data privacy attorney yet?
Afterwith Chief Data Officer Plan Review
CDO REVIEW: Productized Customer Data Revenue

Risk Flag Summary: HIGH-RISK decision, requires sequential legal + technical gates

DECISION QUESTION 1: DATA RIGHTS
   Q: Do your existing customer contracts permit you to productize their data?
   Risk: If NO, you expose yourself to breach-of-contract claims.
   Action: Have legal audit EVERY customer agreement (not just template) → Can you monetize data generated post-sale?
   Red flag: Customers acquired via reseller channel often have restrictive data clauses.
   Decision gate: DO NOT PROCEED unless ≥90% of revenue comes from contractually permissive agreements.

DECISION QUESTION 2: PRIVACY REGULATION JURISDICTION
   Q: What % of customer data is EU-sourced (GDPR), California-sourced (CPRA), Canada-sourced (PIPEDA)?
   Risk: Severe. Fines = 4% of annual revenue (GDPR) or loss of data access entirely.
   Action: Map customer locations → Compliance requirements per jurisdiction.
   Red flag: If >20% EU revenue, GDPR anonymization is the only option (kills product value).
   Decision gate: EU GDPR path requires true anonymization (test: Can you re-identify individuals? If yes, still PII).

DECISION QUESTION 3: DATA ARCHITECTURE SEPARATION
   Q: Can you technically guarantee customer data ≠ productized data sets?
   Risk: Accidental data leakage → Product gets your customer's secret data → Lawsuit.
   Action: Diagram data flows. Separate databases? Separate AWS accounts? Encryption + key segregation?
   Red flag: Single monolithic DB = unsafe. Multi-tenant architecture = high leakage risk without vault architecture.
   Decision gate: Require independent security audit of data isolation before launch.

DECISION QUESTION 4: COMPETITIVE MOAT + MARKET SIZING
   Q: Who else has this data? What's your differentiation?
   Risk: Data commodity market collapses pricing. If 5 competitors sell same data, margin erodes 40-60%.
   Action: Competitive audit → Can you get exclusive distribution rights from customers?
   Red flag: If your data is available from 3 other vendors at lower cost, this is a losing bet.
   Opportunity: Can you be the FIRST? If yes, 18-month window for pricing power before commoditization.

DECISION QUESTION 5: CUSTOMER TRUST & CHURN
   Q: If customers learn you're selling data about THEIR behavior, will they churn?
   Risk: Perceived betrayal → churn rate increases 15-30% (real case: ex-social platforms).
   Action: Survey 20 power-user customers (confidential) → "If we offered anonymized data insights to third parties, would you stay?"
   Red flag: If <70% say yes, churn risk is material. Delay until you've built deeper moat.
   Decision gate: Require customer opt-in (don't just assume). Publish privacy policy change 60 days in advance.

TIMELINE TO DECISION:
   Week 1: Legal review of top 10 customers + jurisdiction mapping
   Week 2: Security architecture + anonymization feasibility study
   Week 3: Competitive + customer sentiment research
   Week 4: GO/NO-GO vote

RECOMMENDATION:
   This is viable IF you have exclusive, permissive data rights + strong anonymization tech. If not, it's a distraction.
   Timeline: 6 weeks to make decision, not 6 weeks to launch.

About this skill

Before you train on, sell, share, or migrate data, check whether you have the right to, whether the data is good enough, and whether anyone owns it. Legal questions are framed precisely for counsel, never stated as fact.

What it does

  • Classifies every source's rights status: owned, contracted, licensed, scraped, ambiguous
  • Checks privacy: pseudonymization limits, deletion across derived data and models, impact assessments
  • Checks fitness for purpose, label quality, lineage, and metric definitions
  • Reviews warehouse, lakehouse, and mesh choices against team and workloads
  • Sequences data hiring to the real bottleneck

What you get

A proceed / proceed with conditions / pause / stop verdict, a data source table, ranked risks with fixes, questions for counsel, and what would change the verdict.

Best for

Founders, product and data leaders, and AI teams planning model training, data licensing, data sharing deals, or data platform migrations.

SKILL.md preview

SKILL.md
---
name: cdo-review
description: Critique one data plan - using customer data to train or fine-tune a model, licensing or selling a data product, a warehouse or lakehouse migration, a new metrics layer, a data sharing deal, or a data team hiring plan - through a Chief Data Officer's lens and return a verdict, data rights and privacy gaps, data quality and lineage risks, governance holes, and what would change the verdict. Use when the user says "can we train on our customer data?", "review our data licensing plan", "poke holes in our warehouse migration", "is our data good enough for this?", "check our data team hiring plan", or "would a Chief Data Officer approve this?".
---

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