Auto Data Validator
Upload a CSV and this tool automatically figures out which columns look like emails, dates, URLs, or phone numbers — then flags every value that doesn't match.
Detected Column Formats
Flagged Values
How to use Auto Data Validator
- Start by providing the required input: Drop a CSV file. Use Load Sample to try example data first.
- No additional settings need to be configured.
- This tool has no separate run button; its results update as you add or change the input.
- Review the results, then choose an output control: Export Flagged Values.
Spotting the one bad email address in a thousand rows
A column of two thousand email addresses with three malformed entries looks completely fine until it bounces in a mail campaign or breaks an import — and finding those three by scrolling is impractical. Auto-detecting each column's likely format and flagging exceptions surfaces exactly those outliers.
How format detection avoids false positives
A column is only treated as a known format (email, date, URL, etc.) if a strong majority of its non-empty values already match that pattern — a handful of malformed entries don't prevent detection, which is precisely what lets this tool find them.
What formats are detected?
Email, URL, phone number, date, and plain numeric/integer columns — whichever pattern best matches each column's actual data.
What if a column doesn't match any known format?
It's reported as "Unrecognized (free text)" and simply isn't checked for format violations — not every column needs to fit a pattern.
Need custom validation rules instead of auto-detection?
Use the Data Validation Tool, where you define required fields, patterns, and uniqueness rules yourself.