CSV Data Cleaner

Common CSV Problems This Tool Fixes

Structural Issues

• Inconsistent column names (First Name vs first_name vs FirstName)
• Missing column headers
• Extra blank rows at top or bottom
• Inconsistent row lengths (some rows have more fields than others)

Data Quality Issues

• Duplicate rows (exact or near-duplicate)
• Mixed date formats (01/01/2024 vs 2024-01-01 vs January 1, 2024)
• Inconsistent case (LONDON vs London vs london)
• Extra whitespace in values
• Encoding issues (weird characters from Windows Excel export)

Missing Values

• Empty cells that should have a default value
• NULL vs empty string inconsistency
• Placeholder values like "N/A", "n/a", "none", "-" not treated as empty

CSV in Automation Workflows

CSV is the most common format for importing data into CRMs, email tools, and databases. Dirty CSV causes import failures, duplicate contacts, and data quality issues that compound over time.

Clean CSV before every import — especially for:

• CRM bulk imports (HubSpot, Salesforce, Pipedrive) — see CRM Automation for Small Businesses
• Email list imports (Mailchimp, ConvertKit)
• Database uploads
• Reporting tool data sources — the Make.com Google Sheets Integration guide covers a common destination for cleaned data

Automating CSV Cleaning

For recurring CSV imports (weekly sales reports, monthly contact lists), manual cleaning doesn't scale. I can build an automated pipeline that receives a CSV, cleans it, validates it, and loads it into your destination system. Book a consultation to discuss.