Agent skill · product strategy · borghei
chief-data-officer-advisor
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.
Why this skill is useful
Adds executable scripts for data maturity assessment, governance auditing, and platform evaluation that are tailored to specific industry standards.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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 Workflows Workflow 1 — Score data maturity 1. Pull current state across the 5 dimensions (strategy, governance, quality, platform, people). 2. Run datamaturityassessor.py against the populated JSON. 3. Translate prioritized gaps into a quarterly OKR for the data org. Workflow 2 — Audit the data governance program 1. Inventory domains, policies, controls, owners, evidence. 2. Run datagovernanceaudit.py to score against a DAMA-DMBOK-aligned control set. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/chief-data-officer-advisor