CSV Column Mapping Checklist

CSV Column Mapping Checklist helps a small SaaS decide whether an uploaded file can be interpreted safely before creating records.

Start with required fields. Every import needs a small set of fields that make a row meaningful: customer email, SKU, order id, account id, start date, amount, status, or whatever the product treats as identity. The preview should show which required fields were found, which were guessed, and which are missing. Guessing should be visible because a wrong guess can be worse than a hard failure.

Next handle aliases. Users rarely name columns exactly as the product expects. “email,” “customer email,” “mail,” and “contact” may refer to the same field, but the product should show the mapping rather than silently accepting it. A mapping screen should let users confirm, change, or ignore each column. Extra columns should be listed as ignored, not hidden.

Then validate values by field type. Dates need format detection. Currency values need symbols, separators, and decimals handled carefully. IDs need leading zeros preserved. Emails need syntax checks but should not be over-validated against live deliverability. Status fields need accepted values or a mapping table. Empty cells need separate treatment from zero, false, or intentionally blank.

After that, check row identity. Does the import create new rows, update existing rows, merge duplicates, or reject duplicates? If the product supports update imports, the preview should show how many rows match existing records and which key was used. If the key is ambiguous, the import should stop before writing data.

Finally show consequence counts: total rows, valid rows, rows with errors, rows with warnings, rows skipped, records created, records updated, and columns ignored. This checklist keeps CSV import from being a blind upload button.

CSV Column Mapping Checklist

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