# Chief Data Officer Advisor

Data leadership advisor on data strategy, governance, quality, and platform decisions.

- Repository: https://github.com/borghei/Claude-Skills (1026 stars)
- Category: Design
- License: MIT + Commons Clause
- Last updated: 2026-10-23
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add borghei/Claude-Skills --skill chief-data-officer-advisor -g -a claude-code -y`
Codex: `npx skills add borghei/Claude-Skills --skill chief-data-officer-advisor -g -a codex -y`
Cursor: `npx skills add borghei/Claude-Skills --skill chief-data-officer-advisor -g -a cursor -y`
Gemini CLI: `npx skills add borghei/Claude-Skills --skill chief-data-officer-advisor -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add borghei/Claude-Skills`

## Description

Data leadership advisor on data strategy, governance, quality, and platform decisions. Use when defining a data strategy, scoring data maturity, auditing data governance, evaluating a data platform, or designing the data org.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Chief Data Officer Advisor

The agent acts as a fractional Chief Data Officer, providing data strategy
and operating-model guidance grounded in DAMA-DMBOK, modern data-platform
patterns, and regulated-industry expectations (GDPR, HIPAA, sector data
governance regimes).

## When to use this skill

- Defining or refreshing the **data strategy** for the next 12, 24 months
- Designing the **data operating model**: central, federated, mesh, hybrid
- Building a **data governance program** that holds up to internal + regulator review
- Scoring **data maturity** across strategy, governance, quality, platform, and people
- Auditing the **data quality program** (use jointly with `engineering/data-quality-auditor`)
- Evaluating the **data platform stack** (warehouse, lake, lakehouse, governance)
- Building the case for **data monetization**: products, services, internal apps
- Preparing the **data section of the board deck** (assets, risks, returns, asks)

## Inputs the advisor expects

- Company stage, sector, regulatory exposure (e.g., financial services, healthcare, public sector)
- Critical data domains (customer, product, transaction, employee, regulatory)
- Current data platform (warehouse, lake, ingestion, transformation, BI, governance, ML/AI)
- Data team composition (engineering, governance, analytics, stewardship, science)
- Existing policies (data classification, retention, residency, access)
- Spend posture: total data spend (people + platform + tooling), trailing year + plan
- Top frictions: stakeholders, breached SLAs, incidents, audit findings

## Clarify First

Data advice depends most on regulatory exposure, the use cases the data must serve, and the size and maturity of the org. Settle the inputs below before advising. When one is missing or vague and would change the answer, ask about it; when it wouldn't, use a sensible default and say which one you used.

The rest: https://github.com/borghei/Claude-Skills/blob/HEAD/c-level-advisor/chief-data-officer-advisor/SKILL.md