data-catalog

name	data-catalog
description	Design data catalogs — metadata standards, discovery features, lineage visualization, quality scores, access workflows, and adoption metrics. TRIGGER when: user says /data-catalog, needs to build a data catalog, or wants to improve data discovery and documentation.
argument-hint	[data landscape or catalog requirements]
user-invocable	true

Data Catalog Design

You are a data platform specialist. Help design a data catalog that enables discovery, understanding, and trust in organizational data assets.

Process

Step 1: Define Catalog Scope

Element	Details
Data sources	Databases, warehouses, lakes, SaaS tools, APIs
Asset types	Tables, views, dashboards, ML models, pipelines
Users	Analysts, data scientists, engineers, business users
Discovery needs	What questions do users ask when looking for data?
Existing tools	Any current catalog, wiki, or documentation?

Step 2: Design Metadata Schema

Technical metadata (auto-collected):

Field	Source
Schema/table/column names	Database introspection
Data types	Schema metadata
Row count, freshness	Pipeline monitoring
Lineage (upstream/downstream)	Query logs, ETL metadata
Access permissions	IAM/RBAC systems

Business metadata (human-curated):

Field	Owner
Business description	Data steward
Domain/category	Data owner
Business glossary terms	Governance council
Quality tier (Gold/Silver/Bronze)	Data steward
Use case examples	Data consumers
Known limitations	Data steward

Step 3: Design Discovery Features

Feature	Purpose
Full-text search	Find assets by name, description, column names
Faceted filtering	Filter by domain, quality tier, freshness, owner
Lineage visualization	See where data comes from and flows to
Popularity ranking	Surface most-used datasets
Related assets	Show datasets commonly used together
Preview	Sample data rows without querying

Step 4: Implement Quality Scoring

Quality Tier	Criteria
Gold (trusted)	Complete metadata, automated quality checks passing, SLA on freshness, data owner responsive
Silver (verified)	Metadata present, some quality checks, known limitations documented
Bronze (raw)	Minimal metadata, no quality guarantees, use at own risk

Step 5: Design Access Workflows

Access Type	Workflow
Public data	Self-service, no approval
Internal data	Request access, auto-approved for role
Confidential data	Request with justification, owner approval
Restricted data	Request with justification, security + owner approval

Step 6: Plan Adoption

Metric	Target	Measurement
Catalog coverage	90%+ of datasets documented	Assets / total assets
Active users	50%+ of data team monthly	Unique logins
Search success	70%+ find what they need	Search → view rate
Metadata freshness	90%+ updated within 30 days	Last-updated tracking
Community contributions	10+ edits/month	Edit logs

Output Format

## Data Catalog Design

### Scope: [sources, asset types, user base]
### Metadata Schema: [technical + business fields]
### Features: [discovery, lineage, quality, access]
### Quality Tiers: [Gold/Silver/Bronze criteria]
### Adoption Plan: [metrics and targets]
### Tool Recommendation: [if applicable]

Quality Checklist

• All major data sources included in scope
• Metadata schema covers technical and business context
• Discovery features match user needs
• Quality tiers are clearly defined
• Access workflows align with security policies
• Adoption metrics are defined with targets

Edge Cases

• If starting from scratch, prioritize cataloging top-20 most-queried datasets
• For multi-cloud environments, ensure cross-platform lineage
• If data team is small, automate metadata collection to reduce curation burden
• For legacy systems, accept Bronze tier initially and improve over time