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
Check headers and row shape
Review columns, delimiters, empty rows, duplicate headers, and whether every row has the expected shape.
Normalize values before conversion
Clean casing, whitespace, repeated values, emails, and known placeholder strings before creating the final import file.
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.
Recommended Tools
Open the browser tools that support this workflow.
CSV to JSON Converter
Convert CSV spreadsheet data to JSON
OpenJSON→CSVJSON to CSV Converter
Convert JSON arrays to CSV spreadsheet format
OpenDe-DupeRemove Duplicate Lines
Remove duplicate lines from text
OpenClean TextText Cleaner
Remove extra spaces, fix line breaks, and normalize whitespace
OpenEmail ValidatorEmail Address Validator
Validate email addresses for correct format and common typos
OpenFind/ReplaceFind & Replace Text
Advanced find and replace with regex, case-sensitive, and whole word options
OpenJSON ValidateJSON Validator
Validate JSON syntax and find errors
OpenRelated Conversion Guides
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OpenConversionJSON to CSV Converter
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OpenConversionJSON Formatter & Validator
Format messy JSON with proper indentation. Validate syntax, find errors, minify for production.
OpenConversionFix: JSON Parse Error
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OpenRelated Guides
Read deeper guidance that supports this workflow.
Developer Data Format Guide
Compare JSON, CSV, XML, YAML, Base64, URL encoding, and hashes for cleaner developer workflows and safer data handoffs.
OpenGuideBrowser-Based Developer Tools
Use browser-based developer tools for formatting, validation, encoding, generators, and quick inspections with clearer data boundaries.
OpenGuideJSON Formatter Privacy Guide
Format, validate, and share JSON safely by removing tokens, customer data, internal URLs, and production secrets before using developer tools.
OpenRelated Formats
Review file format decisions that affect this workflow.
CSV Tools and Format Guide
Convert, inspect, and prepare CSV data for spreadsheets, APIs, exports, imports, reports, and JSON workflows.
Open.jsonJSON Tools and Format Guide
Format, validate, inspect, convert, and clean JSON data for APIs, configs, logs, debugging, and safer sharing.
Open.md, .markdown, .mdownMarkdown Tools and Format Guide
Write, preview, convert, and clean Markdown for docs, READMEs, changelogs, notes, websites, and HTML publishing.
OpenRelated Collections
Browse nearby groups of tools for adjacent tasks.
Developer Formatting and Validation Tools
Format, validate, convert, and inspect JSON, CSV, YAML, XML, HTML, SQL, regex, and other developer data formats.
Open8 toolsText and Document Cleanup Tools
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Open9 toolsBrowser-Based Developer Tools
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OpenWorkflow 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.