Erstellen a backup of the original workbook before changing anything.
Data cleanup guide
How to Clean Excel Data with AI
Messy spreadsheets usually contain a mix of blank cells, inconsistent labels, duplicate rows, broken dates, and formatting that makes analysis harder than it should be. This guide shows a safer way to clean the file without losing the original structure.
Step by step
A reliable workflow you can repeat.
Hochladen the file and review the detected columns, data types, and quality issues.
Standardize dates, names, categories, and number formats before removing records.
Prüfen duplicate and blank-row suggestions instead of deleting everything automatically.
Exportierenieren the cleaned file and compare totals with the original workbook.
Keep the result accurate.
- Clean one problem type at a time so changes are easier to verify.
- Keep identifiers such as invoice numbers and customer IDs as text when leading zeros matter.
- Check totals and row counts after every major cleanup step.
Move from the guide into the working tool.
Use the relevant workflow, review the preview carefully, and export only after the result matches your source data and business rules.
Open DatenqualitätsprüfungFAQ
Häufige Fragen about this workflow.
Can AI clean a large Excel file?
It can help identify patterns and propose corrections, but large files should still be reviewed in sections and validated after export.
Will cleaning change my original file?
Use a copied file or a workflow that produces a new downloadable result so the original remains available for comparison.
What should I check before deleting duplicates?
Confirm which columns define a true duplicate and whether repeated rows represent separate transactions.