dataclean.to

Clean Ecommerce Product Data from Google Sheets

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12

Google Sheets is a go-to tool for collaborative product data management, but shared editing creates unique data quality challenges. Multiple editors introduce inconsistent formatting, locale-dependent number formats, and accidental formula overwrites. dataclean.to imports your Google Sheets product data and resolves the issues that collaborative editing creates, producing a clean product catalog.

The Problem

Google Sheets product data suffers from locale-specific formatting: European editors enter prices as 19,99 while US editors use 19.99. Dates appear as 02/03/2026 (Feb 3 or March 2?). Collaborators paste data from external sources bringing invisible characters, different quote marks, and inconsistent line breaks. Data validation rules get bypassed through paste operations. IMPORTRANGE formulas return #REF errors when source sheets are deleted. Google Sheets import/export documentation

How to Fix It

1
Export or connect your Google Sheet
Download your product sheet as a CSV, or connect directly through dataclean.to's Google Sheets integration. All columns, including those with formulas, are captured.
2
Resolve locale formatting conflicts
Detect and normalize decimal separators (comma vs period) in price columns. Standardize date formats to remove US/EU ambiguity. Convert locale-specific number formats to a consistent standard.
3
Fix collaborative editing artifacts
Remove invisible characters pasted from external sources. Standardize quote marks (curly to straight). Fix inconsistent capitalization introduced by different editors. Replace broken formula references with static values.
4
Validate product data integrity
Check for missing required fields across all products. Flag duplicate SKUs or product names. Verify price values fall within expected ranges. Validate URL fields for product images and links.
5
Export clean data
Download the standardized product data as CSV or push it back to a clean Google Sheet. The data is consistent regardless of which editor or locale originally entered it.

Frequently Asked Questions

How does dataclean.to handle the comma vs period decimal separator issue?
The platform detects the decimal convention used in each cell by analyzing patterns across the column. If a price column has both '19,99' and '19.99', it normalizes all values to your chosen format.
Can I clean data directly in my Google Sheet?
dataclean.to works on a copy of your data. After cleaning, you can download the result and paste it back into your Google Sheet, or export to CSV for import into your ecommerce platform.
What about Google Sheets formulas?
Formula results are exported as static values. Broken formulas (#REF, #ERROR, #N/A) are flagged and can be replaced with defaults or left blank depending on your preference.

Example: Input → Output

nameskupricebrandrating
Wireless Headphones ProWHP-001$89.99SoundTech4.5
wireless headphones proWHP00189.99soundtech4.5/5

Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Wireless Headphones Pro",
  "sku": "WHP-001",
  "offers": {"@type": "Offer", "price": "89.99", "priceCurrency": "USD"},
  "brand": {"@type": "Brand", "name": "SoundTech"},
  "aggregateRating": {"@type": "AggregateRating", "ratingValue": "4.5"}
}
💡 How it works: Product schema enables rich results with price, availability, and star ratings in Google Shopping.

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