Chief Data Officer Advisor
Chief Data Officer Advisor provides strategic data leadership for startups, covering four key decisions: whether customer data can be used for AI training, choosing data architecture (warehouse vs lakehouse vs mesh) and build-vs-buy, valuing customer data as an asset for M&A or monetization, and planning data team org evolution. It produces stage-driven recommendations and decision frameworks, not tactical engineering guidance.
✨ What it does
- Audits data sources for AI training eligibility using a three-dimensional matrix (origin, data class, use case) to outpu
- Recommends data architecture based on stage (warehouse, lakehouse, mesh) and provides a per-layer build-vs-buy table.
- Values B2B customer data as an asset, including strategic value, M&A multiplier, and productization paths with risk adju
- Maps data team roles to company stage and identifies the centralize-vs-embed trigger.
- Provides output standards with bottom line, decision, evidence, action steps, and founder's call.
- Includes Python scripts for automated audits and strategy pickers.
🎯 When to use it
- Deciding whether to train AI models on customer data, considering consent and legal provenance.
- Choosing between warehouse, lakehouse, or data mesh architecture and what to build vs buy.
- Valuing customer data for fundraising, M&A readiness, or productization.
- Sequencing data hires and deciding whether to centralize or embed data roles.
- When the user mentions CDO, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset.
🚀 How to use
Trigger: /cs:decide
Trigger by invoking the skill via slash command /cs:cdo-advisor or by asking a strategic data question (e.g., 'Can we train on this data?', 'What data architecture should we use?', 'What is our data worth?'). Provide context such as data source inventory, company stage, or business decisions. Example prompts:
Can we use our customer data to train our model? Here is a list of sources.
We are a Series B SaaS. Should we move to a lakehouse? What should we build vs buy?
📄 Output: A decision brief with bottom line, decision framing, evidence, action steps, and the founder's call, plus optional Python script outputs.
📦 Add this skill to Claude Code
# 1. Get the skills repo
git clone --depth 1 https://github.com/alirezarezvani/claude-skills /tmp/claude-skills
# 2. Copy this skill into your project (or ~/.claude/skills for all projects)
mkdir -p .claude/skills
cp -r /tmp/claude-skills/c-level-advisor/skills/chief-data-officer-advisor .claude/skills/chief-data-officer-advisorSkill source: c-level-advisor/skills/chief-data-officer-advisor/SKILL.md
⚠️ Good to know
This skill is strategic only and does not cover tactical data engineering; decisions involving legal or regulatory matters require qualified counsel.
❓ FAQ
What does the skill consider when deciding if we can train on customer data?
It uses a matrix of three dimensions: origin (e.g., explicit opt-in, TOS-only, scraped), data class (e.g., PII, behavioral), and use case (e.g., personalization, fine-tuning). Each combination yields GO, MITIGATE, or NO-GO.
How does the skill decide between warehouse, lakehouse, and mesh?
The choice is stage-driven: warehouse for ≤5 consumers and <2TB, lakehouse for 5-25 consumers and 2TB-1PB with ML use cases, and mesh for 25+ consumers across 4+ domains with federated ownership.
What is the centralize-vs-embed trigger?
When 3+ functional areas need bespoke data weekly, the central team becomes a bottleneck, so you should move to hub-and-spoke with embedded analysts.
🤖 Overview, features, install steps and FAQ were generated from the project's SKILL.md on Sep 4, 2026. Always check the original source before running commands.