Data Quality Assessment

---
name: data-quality-assessment
description: "Use when assessing data quality."
version: 1.0.0
author: Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [data-science, data-quality, profiling, validation]
    related_skills: ['data-profiling-quality']
---

## Overview
Assess data quality using profiling metrics and validation rules.

## When to Use
- "Data Quality Assessment design and architecture"
- "Best practices for Data Quality Assessment"
- "Data Quality Assessment implementation and deployment"
- "Data Quality Assessment optimization and monitoring"
- "Data Quality Assessment troubleshooting and scaling"

## Key Concepts
1. Foundational concepts
2. Implementation approaches
3. Testing and validation

## Implementation Patterns
1. Define clear requirements and specifications
2. Choose appropriate tools and frameworks
3. Implement with modular, maintainable code
4. Write tests and automate verification
5. Document architecture and decisions
6. Monitor performance and iterate

## Common Pitfalls
1. **Not accounting for constraints** — resource or timeline limitations
2. **Ignoring industry standards** — not following established best practices
3. **Poor stakeholder alignment** — conflicting requirements
4. **Inadequate testing** — no validation of critical functions
5. **Not documenting decisions** — lost knowledge transfer

## Verification Checklist
- [ ] Requirements documented
- [ ] Standards reviewed
- [ ] Design validated
- [ ] Testing established
- [ ] Documentation complete