DataDoctor

Runs entirely in your browser — no uploads, no saved snippets, no public links.

Paste a file, inspect the issues, export reviewed output.

CSV import preflight

CSV Data Quality Checker

Profile missing values, duplicates, invalid formats, mixed types and import blockers before using your CSV.

Paste a CSV file or load the included customer sample.

Parsed locally with PapaParse. The browser tool does not upload snippets.

Next workflow

Continue the preflight

Fix CSV structure

Check malformed rows, delimiters and broken quotes.

Remove duplicates

Deduplicate rows and export a cleaned CSV.

Validate schema

Check every row against a JSON Schema contract.

After the tool runs

CSV Data Quality Checker review guide

Use the tool above first. The supporting notes below help you interpret the result, fix the right issues in the right order, and choose the next DataDoctor tool without pushing SEO content above the actual task.

Best input

profiling a CSV for import readiness before uploading it to a CRM, store, database or API.

Output to keep

Save the original file, the issue report and the reviewed export as separate files.

Next check

After structural and quality issues are visible, run a platform checker or schema validator before upload.

What it checks

CSV Data Quality Checker for real data work

CSV Data Quality Checker should sit before the import screen, not after a failed upload. It turns hidden spreadsheet problems into a checklist you can review row by row.

  • Missing cells
  • Duplicate rows
  • Invalid emails, URLs and dates
  • Mixed-type columns and outliers

Fix these first

Common errors to review before downstream work

Most failures come from small file issues that become expensive only after an API call, import job or spreadsheet cleanup. Fix blocking errors first, then re-run the same tool before moving forward.

  • Blank required values
  • Duplicate records
  • Whitespace that breaks matching
  • Numbers or dates stored inconsistently

Recommended workflow

Run the check in this order

Treat any downloaded output as a reviewed candidate. Keep the source CSV unchanged so you can reconcile removed rows, duplicate groups or missing values later.

  1. Step 1

    Load the CSV

  2. Step 2

    Run the quality report

  3. Step 3

    Review critical issues first

  4. Step 4

    Export the report or continue to a cleaner or platform checker

How to interpret a passing result

A pass means this specific preflight did not find the issues listed above. It is not a guarantee that the target system will accept every row, field, custom mapping or account-specific rule.

Do not clean, deduplicate or drop rows before parser errors, required columns and duplicate-key logic are clear.