Data Quality Analyzer¶
Interactive Tool
Quick Reference
- Dimensions: Completeness, uniqueness, validity, consistency, documentation
- Input: CSV or Excel files
- Output: Quality score with column-level metrics and issues
- Related: Data Quality Template
Overview¶
The Data Quality Analyzer evaluates datasets across five key dimensions:
| Dimension | Description |
|---|---|
| Completeness | Missing values, null rates, coverage |
| Uniqueness | Duplicates, key integrity, cardinality |
| Validity | Format compliance, range constraints, type consistency |
| Consistency | Cross-field validation, business rules |
| Documentation | Metadata quality, lineage, dictionary |
Features¶
- Upload CSV/Excel files for analysis
- Column-level quality metrics
- Automated issue detection
- Quality score calculation
- Exportable reports
Quality Thresholds¶
| Score | Rating | Action |
|---|---|---|
| 90-100 | Excellent | Ready for production |
| 70-89 | Good | Minor improvements needed |
| 50-69 | Fair | Remediation required |
| <50 | Poor | Significant data work needed |
When to Use¶
- Before model training
- Data pipeline validation
- Vendor data assessment
- Ongoing monitoring