Data quality review.
From source inconsistencies to a verified output preview.
The problems are in the details.
| ID | Name | Date | Amount |
|---|---|---|---|
A-001 | North Workshop | 11/01/2026 | 125.00 |
A-002 | Harbor Studio | 2026-11-02 | 240.50 |
A-002 | Harbor Studio | 2026-11-02 | 240.50 |
A-003 | Missing name | 11/03/2026 | 90.00 |
A-004 | Pine Office | 2026-11-03 | 175.25 |
Every correction.<br/>Every exception.
Compare original and normalized values, inspect the accepted table, and see why two records are held back.
Run the example above. Nothing is downloaded.