What types of data can the Data Standardizer clean?

The Data Standardizer can clean and normalize multiple data types including: text numbers (converting 'sixteen' to 16), dates in various formats, phone numbers, boolean values (yes/no, true/false), currency, email addresses, names with proper capitalization, time formats, US states (California to CA), countries (United States to US), months (January to Jan), days of the week (Monday to Mon), and text casing. It automatically detects the data type in each column.

Is the Data Standardizer tool really free to use?

Yes! The Data Standardizer is completely free to use with no subscriptions, no registration required, and no hidden costs. Process unlimited data, upload files, and export your cleaned results - all for free.

Can I upload CSV or Excel files to clean?

Yes, you can upload CSV and TSV files directly to the tool. The standardizer will parse your file, detect data types in each column, and allow you to clean and normalize all the data at once. You can then download the cleaned results.

How does the auto-detection feature work?

The tool analyzes sample values from each column to identify patterns and determine the most likely data type. It looks for numeric patterns, date formats, phone number structures, boolean indicators, and more. You can always override the auto-detection if it's incorrect.

Will my data be stored or shared?

No, all data processing happens entirely in your browser. Your data is never uploaded to any server or stored anywhere except in your browser's local storage (if you choose to save preferences). Your privacy is protected.

Free Online Data Standardizer Tool (No Signup)

Step 1: Input Your Data

Choose Input Method:

Step 2: Review Detected Data Types

Review the auto-detected data types below. You can override any detection by selecting a different type from the dropdown.

Step 3: Results & Statistics

✅ Data standardization complete! Review the changes below.

🔧 Data Standardizer & List Cleaner

Operations

Step 1: Input Your Data

Step 2: Review Detected Data Types

Step 3: Results & Statistics

Changes by Column

Data Preview (First 50 rows)