data-catalog-and-discovery
Build and operate data catalogs so people can find, understand, and trust datasets — with metadata, ownership, and certification signals. Use when analysts cannot find the right table, or when enabling self-serve analytics at scale.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- data-catalog-and-discovery
- description
- Build and operate data catalogs so people can find, understand, and trust datasets — with metadata, ownership, and certification signals. Use when analysts cannot find the right table, or when enabling self-serve analytics at scale.
03 SKILL.md 正文
Data Catalog and Discovery
Overview
A data catalog makes data findable and understandable. Without discovery, organizations recreate the same extracts and argue about which file is right.
When to Use
- Enabling self-serve analytics
- Reducing duplicate datasets
- Onboarding new analysts faster
- Certifying official sources for key metrics
Core Practices
- Inventory important datasets with owners and descriptions
- Capture business meaning, not only technical schema
- Mark certification tier (raw, curated, certified)
- Support search by business term and metric name
- Keep metadata fresh via automation where possible
- Measure adoption: search success, certified asset usage
Principles
- A stale catalog is worse than no catalog — it misleads
- Business glossary + technical catalog belong together
- Ownership visibility drives accountability
- Catalogs succeed when embedded in analyst workflow
Verification
- [ ] Critical assets are documented with owners
- [ ] Certification status is visible
- [ ] People can find canonical sources for key metrics
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