Diagnose quotes and embedded line breaks in a Jira CSV import

Jira needs valid record boundaries and a Summary header

Jira's CSV importer uses the heading row to map fields and requires a Summary column. Commas and line breaks inside a field must be enclosed in double quotes; otherwise one issue can become several rows or columns.

• A description contains a line break: keep the full field inside quotes and diagnose the row boundary.
• The preview has too many columns: check commas inside unquoted text and delimiter settings.
• Jira cannot map fields: compare header names with the fields available in the target space.

• Diagnose CSV structure before changing values →Review column counts, quotes, empty rows, and duplicate rows in a separate workspace.
• Diagnose quoted newlines before a Jira import →Check record boundaries and the required Summary header before mapping fields.
• Check or convert the CSV character encoding →Use this when Japanese, Korean, or accented text is garbled after export.

Check encoding, delimiter, and required headers

Jira Cloud defaults to UTF-8 and lets administrators set another delimiter in the Advanced section. Before importing, inspect the first line for Summary and confirm whether the source uses commas, semicolons, or tabs.

• Use UTF-8 unless the Jira import screen and source file require another encoding.
• A header should not contain punctuation that prevents field matching.
• For large projects, Jira recommends smaller batches; preserve issue boundaries when splitting later.

• Find CSV rows with inconsistent column counts →Diagnose malformed rows before uploading them to a platform.
• Check or convert the CSV character encoding →Use this when Japanese, Korean, or accented text is garbled after export.
• Learn the general CSV split workflow →Choose a row count or target size while keeping the header in every output file.

Run a diagnosis before opening Jira's importer

The diagnosis can prove structural consistency and create a separate CSV for selected safe fixes. It cannot verify Jira field semantics, account IDs, or whether a comment will be public.

1. 1Export the source project data and create a working copy.
2. 2Open the copy in LocaCSV's CSV diagnostic workspace and inspect quote, delimiter, and column-count findings.
3. 3If the diagnosis stops at an unclosed quote, return to the source export or edit that record deliberately; do not assume an automatic repair.
4. 4When the structure is consistent, open Jira's importer and set the matching encoding and delimiter in Advanced.

Verify issue counts and field mapping

• Every data row has the same number of fields as the header after quoted content is parsed.
• Summary is present and each issue's description or comment stays in one field.
• The number of issues in the local file matches the Jira preview or import log.
• A small test import confirms project, work type, and account mapping before the full batch.

• Diagnose CSV structure before changing values →Review column counts, quotes, empty rows, and duplicate rows in a separate workspace.
• Learn the general CSV split workflow →Choose a row count or target size while keeping the header in every output file.

If Jira still rejects the file, separate structure from mapping

A structurally valid CSV can still fail because of a missing Summary value, an unknown account ID, an unavailable work type, or a field value that needs mapping. Use Jira's detailed import log for those cases instead of repeatedly changing quotes.

• Check or convert the CSV character encoding →Use this when Japanese, Korean, or accented text is garbled after export.
• Select the columns required by the import template →Remove export-only fields while keeping the original row count.
• Fix duplicate emails before a Mailchimp import →Remove repeated addresses locally, then review Mailchimp's import history.

Give Jira a CSV with traceable rows

Keep the original export, diagnosis result, and Jira import log together so every issue count and mapping decision can be audited.