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Data Cleanup Before Import Workflow

A workflow for checking headers, duplicate rows, invalid values, encoding problems, and conversion structure before an import fails.

Audience

People preparing exports, mailing lists, reports, CRM data, fixtures, and spreadsheet handoffs.

Outcome

Cleaner import files with validated structure, fewer duplicate rows, and safer placeholder data for sharing.

Workflow Steps

Step 1

Check headers and row shape

Review columns, delimiters, empty rows, duplicate headers, and whether every row has the expected shape.

Step 2

Normalize values before conversion

Clean casing, whitespace, repeated values, emails, and known placeholder strings before creating the final import file.

Step 3

Validate the converted output

After converting CSV or JSON, validate the output and compare a few sample rows against the receiving system.

Workflow Notes

  • Spreadsheet software can silently rewrite dates, ids, zip codes, and long numbers.
  • Nested JSON needs intentional flattening rules before it becomes CSV.
  • Public examples should keep the same structure while replacing private row values.

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Workflow FAQs

What causes CSV imports to fail?

Common causes include duplicate headers, quoted commas, blank rows, encoding issues, invalid emails, and spreadsheet auto-formatting.

Should I convert CSV to JSON before import?

Use JSON when the receiving system expects records or nested structure. Keep CSV for flat table imports.

How do I share sample import data safely?

Keep the headers and structure, but replace real names, emails, ids, and customer values with safe examples.