data-normalizer
DataNormalizer is a web service that uses AI to automatically clean and normalize data in CSV and Excel files, correcting typos, inconsistent formatting, capitalization, synonyms, number formats, and other manual input errors. It targets businesses and users needing fast, large-scale data cleaning with pay-as-you-go credit packs and a free trial.
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Quick Overview
Best for: Business Intelligence
What it does
Business Intelligence software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Intelligence
Pricing snapshot
Freemium from $0
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data-normalizer
DataNormalizer is an AI-powered online tool that automates data cleaning and normalization for CSV and Excel files. It corrects typos, shortcuts, inconsistent spellings and capitalization, synonym variations, number-format discrepancies, and other manual input errors to produce consistent, analysis-ready data. The product targets enterprises and individual users who need fast, large-scale cleaning — the site notes more than 10,000,000 rows processed — and offers a free trial and pay-as-you-go credit packs.
AI-powered tool to normalize and standardize data, fixing inconsistencies and errors automatically.
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Claim this listing for $29Key Features
AI-powered normalization
Uses AI to automatically detect and correct typos, shortcuts, and different spellings to normalize data.
Inconsistent formatting fixes
Detects and normalizes formatting differences such as 'Coop' vs 'Co-op'.
Shortcut and abbreviation handling
Normalizes common shortcuts and abbreviations (e.g., 'Limited' vs 'Ltd.').
Spelling and capitalization correction
Automatically corrects spelling mistakes and inconsistent capitalization (e.g., 'Apple' vs 'APPLE').
Synonym resolution
Normalizes synonym variations (e.g., 'Attorney' vs 'Lawyer').
Number formatting normalization
Handles number-format discrepancies like '1,000' vs '1000' vs '1k'.
CSV and Excel support
Accepts CSV files and (now) Excel format for processing.
Batch processing and scale
Designed for large datasets; the site claims more than 10,000,000 rows processed.
Reprocessing on errors
Paid plans include free reprocessing on errors.
Pricing
Free testing tier: $0, up to 25 rows per file, low processing priority, CSV format, no credit card required.
Free
$0- Up to 25 rows per file
- Low processing priority
- CSV format
1,000 Credits
$12- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
5,000 Credits
$29- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
10,000 Credits
$49- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
25,000 Credits
$99- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
100,000 Credits
$249- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
1,000,000 Credits
$990- 1 row = 1 credit
- Unlimited rows
- Fastest processing
- CSV and Excel format
Use Cases
Preparing data for analytics
Clean and normalize manually entered CSV/Excel data to improve accuracy and consistency for downstream analytics and BI tools.
Enterprise data hygiene
Automate large-scale data cleaning for enterprise datasets, reducing manual correction work and improving data quality.
Ad hoc file normalization
Quickly normalize small files or test datasets via the free tier (up to 25 rows per file) for validation and testing.
Integrations
CSV files
Accepts CSV files for normalization and processing.
Excel files
Supports Excel format for upload and normalization (noted as newly supported).
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Sign up or sign in on the Data-Normalizer site.
- 2 Go to the processor and upload a CSV or Excel file (free tier supports up to 25 rows per file).
- 3 Choose between the free trial or purchase credit packs to process larger datasets, then run the normalization and download results.
Support
Contact support and inquire about enterprise plans or student discounts via [email protected].
docs/blog
Blog posts on the site provide guidance and best practices for data cleaning and analytics.
API
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