AI Data Quality Checker
Profile, Score & Clean Your Data
Free online data quality tool. Upload your Excel or CSV file to get instant data profiling, comprehensive quality scores across 7 dimensions (Completeness, Consistency, Uniqueness, Validity, Timeliness, Accuracy, Semantic), and AI-powered smart data cleaning recommendations. No coding required.
How to Check Data Quality in 3 Simple Steps
Upload your Excel or CSV file, get comprehensive data quality scores across 7 dimensions, and apply AI-powered data cleaning in minutes - no coding required
1. Upload Your Data
Upload Excel or CSV files. Get instant data overview with stats and field analysis.
2. Review Quality Score
Get scores for Completeness, Consistency, Uniqueness, Validity, and more.
3. Apply Smart Cleaning
Review and apply AI-generated cleaning plans to fix detected issues.
7 Dimensions of Data Quality Assessment
Comprehensive data quality scoring across industry-standard dimensions including completeness, consistency, uniqueness, validity, timeliness, accuracy, and semantic analysis
Completeness
Measures missing values, null rates, and data coverage across all fields
Consistency
Checks format uniformity, value patterns, and cross-field logical rules
Uniqueness
Detects duplicate records based on single or composite key combinations
Validity
Validates data formats, type constraints, and business rule compliance
Timeliness
Assesses date relevance, currency of data, and temporal consistency
Accuracy
Evaluates value correctness through calculations and reference validation
Semantic
Analyzes logical meaning, context appropriateness, and business relevance
Powerful Data Quality & Profiling Features
Complete data quality management toolkit - from data profiling and validation to duplicate detection, missing value analysis, and automated data cleaning
Instant Data Profiling
Get comprehensive data overview including row counts, column types, null rates, value distributions, and field descriptions in seconds
7-Dimension Quality Scoring
Receive detailed quality scores across Completeness, Consistency, Uniqueness, Validity, Timeliness, Accuracy, and Semantic dimensions
Issue Detection
Automatically identify potential quality issues like inconsistent formats, calculation errors, missing values, and duplicate records
Use Case Suggestions
AI analyzes your data structure and suggests relevant use cases for analysis, reporting, and visualization
Smart Cleaning Plans
Get AI-generated cleaning recommendations with executable code to fix detected issues automatically
Preview Before Apply
Review all cleaning transformations before execution - original data remains unchanged until you confirm
Field Type Detection
Automatic identification of field types including ID, Dimension, Measure, Date, Time, Category, and Amount
Large File Support
Process Excel and CSV files up to 50MB with thousands of rows and columns for enterprise-scale data quality checks
Quality Highlights
Key findings summarized in plain language - excellent completeness, standardized formats, logical consistency, and more
Loved by Data Professionals Worldwide
Data analysts, engineers, and business teams trust ChartGen.ai for data quality management
The 7-dimension quality scoring is a game-changer! It instantly identified consistency issues in our CRM data that we'd been missing for months. The completeness check alone saved us from a major reporting disaster.
Coco X.
Data Analyst at Tech Startup
Finally, a tool that understands data quality beyond just null checks. The semantic analysis detected that our date fields were using inconsistent formats across regions. The AI cleaning fixed it in one click.
Wei H.
Investment Banking at Financial Services
We process thousands of Excel files weekly. The data profiling gives us instant visibility into field types, distributions, and potential issues. The quality score helps us prioritize which files need attention.
Yayako Y.
Brand Storyteller at Digital Marketing Agency
The smart data cleaning is incredible. It generated Python code to remove duplicates based on composite keys, keeping the most complete record. What would have taken hours was done in seconds.
Pravin M.
Student & Developer at University
I use this for every dataset before building charts. The accuracy check caught calculation errors in our financial data - the profit/loss column didn't match market_value minus amount. Saved us from embarrassing mistakes.
Quinn H.
Sales Operations at SaaS Company
The data use case suggestions are surprisingly smart. It analyzed our portfolio data and suggested 8 relevant use cases from performance monitoring to risk assessment. Really helps frame the analysis.
Brenda Z.
Data Storyteller at Early-stage Startup
Uniqueness check found 5 duplicate records we didn't know existed. The AI cleaning plan explained exactly which composite key to use and which record to keep. Clear, actionable recommendations.
Rabinder H.
Mobile Developer at App Development Studio
The validity check is thorough - it detected that our asset_type values were in Chinese and flagged it for standardization. Perfect for preparing data for international reporting.
Richard F.
Co-founder at Fintech Startup
Love the timeliness dimension! It checks if date fields are current and flags potential data gaps on non-trading days. Essential for financial data validation.
Yuzhu P.
Product Manager at E-commerce Platform
The comprehensive data overview is exactly what I need before any analysis. Row counts, column types, null rates, value ranges - all in one glance. The quality highlights section is particularly useful.
A. Ho
Marketing Manager at Retail Brand
Consistency check caught that the same client+account+asset combination had different amount values across dates when they shouldn't. This kind of business logic validation is rare in other tools.
Christine L.
Business Analyst at Consulting Firm
The cleaning execution shows actual code with explanations. I can see exactly what transformations will be applied and even modify them. Transparent AI that I can trust and verify.
Charles W.
Content Creator at Media Company
The 7-dimension quality scoring is a game-changer! It instantly identified consistency issues in our CRM data that we'd been missing for months. The completeness check alone saved us from a major reporting disaster.
Coco X.
Data Analyst at Tech Startup
Finally, a tool that understands data quality beyond just null checks. The semantic analysis detected that our date fields were using inconsistent formats across regions. The AI cleaning fixed it in one click.
Wei H.
Investment Banking at Financial Services
We process thousands of Excel files weekly. The data profiling gives us instant visibility into field types, distributions, and potential issues. The quality score helps us prioritize which files need attention.
Yayako Y.
Brand Storyteller at Digital Marketing Agency
The smart data cleaning is incredible. It generated Python code to remove duplicates based on composite keys, keeping the most complete record. What would have taken hours was done in seconds.
Pravin M.
Student & Developer at University
I use this for every dataset before building charts. The accuracy check caught calculation errors in our financial data - the profit/loss column didn't match market_value minus amount. Saved us from embarrassing mistakes.
Quinn H.
Sales Operations at SaaS Company
The data use case suggestions are surprisingly smart. It analyzed our portfolio data and suggested 8 relevant use cases from performance monitoring to risk assessment. Really helps frame the analysis.
Brenda Z.
Data Storyteller at Early-stage Startup
Uniqueness check found 5 duplicate records we didn't know existed. The AI cleaning plan explained exactly which composite key to use and which record to keep. Clear, actionable recommendations.
Rabinder H.
Mobile Developer at App Development Studio
The validity check is thorough - it detected that our asset_type values were in Chinese and flagged it for standardization. Perfect for preparing data for international reporting.
Richard F.
Co-founder at Fintech Startup
Love the timeliness dimension! It checks if date fields are current and flags potential data gaps on non-trading days. Essential for financial data validation.
Yuzhu P.
Product Manager at E-commerce Platform
The comprehensive data overview is exactly what I need before any analysis. Row counts, column types, null rates, value ranges - all in one glance. The quality highlights section is particularly useful.
A. Ho
Marketing Manager at Retail Brand
Consistency check caught that the same client+account+asset combination had different amount values across dates when they shouldn't. This kind of business logic validation is rare in other tools.
Christine L.
Business Analyst at Consulting Firm
The cleaning execution shows actual code with explanations. I can see exactly what transformations will be applied and even modify them. Transparent AI that I can trust and verify.
Charles W.
Content Creator at Media Company
Frequently Asked Questions
Everything you need to know about AI Data Quality Checker
AI Data Quality Checker is an intelligent tool that analyzes your data files to assess quality across 7 key dimensions: Completeness, Consistency, Uniqueness, Validity, Timeliness, Accuracy, and Semantic. It provides instant data profiling, comprehensive quality scores, identifies potential issues, and offers AI-powered cleaning recommendations to help you improve your data before analysis or reporting.
ChartGen.ai's Data Quality Checker supports Excel files (.xlsx, .xls) and CSV files. Simply upload your spreadsheet or comma-separated values file, and the AI will instantly analyze the data structure, column types, and quality metrics. Files up to 50MB with unlimited rows and columns are supported.
The 7 data quality dimensions are: 1) Completeness - measures missing values, null rates, and data coverage; 2) Consistency - checks format uniformity and cross-field logic across records; 3) Uniqueness - detects duplicate records using single or composite keys; 4) Validity - validates data formats, types, and business rule constraints; 5) Timeliness - assesses date relevance, currency, and temporal consistency; 6) Accuracy - evaluates value correctness through calculations and reference validation; 7) Semantic - analyzes logical meaning, context appropriateness, and business relevance using AI. Each dimension receives a score contributing to the overall data quality rating.
The AI data cleaning feature analyzes detected quality issues and generates a smart cleaning plan with executable code. It can automatically remove duplicates based on composite keys, handle missing values through imputation or removal, standardize formats, fix data type inconsistencies, and resolve outliers. You can preview all changes before applying them, and the original data remains unchanged until you confirm.
The Data Overview provides: dataset summary with row and column counts, complete rows percentage, null rate statistics, automatic field type detection (ID, Dimension, Measure, Date, Category), detailed field descriptions with value ranges, distribution analysis, data quality highlights, industry and use case suggestions, and potential quality issues flagged for review.
The quality scoring uses industry-standard data quality metrics with AI-enhanced analysis. Each dimension is calculated using proven algorithms - for example, Completeness counts non-null values, Uniqueness identifies duplicates via composite keys, and Accuracy validates calculations. The Semantic dimension uses AI to understand context and business meaning, providing insights that rule-based systems miss.
Yes! The Consistency and Accuracy dimensions specifically check for business logic errors. For example, it can detect if calculated fields don't match their formula (like profit_loss not equaling market_value minus amount), if the same entity has inconsistent values across time periods, or if categorical values don't align with expected patterns.
The tool detects: missing values and incomplete records, duplicate rows and near-duplicates, format inconsistencies (dates, numbers, text), calculation errors and formula mismatches, outliers and anomalies, invalid data types, temporal gaps and date issues, cross-field logic violations, encoding problems, and semantic inconsistencies.
The cleaning execution generates Python/pandas code for each transformation step. You can see exactly what operations will be performed - adding tracking columns, identifying duplicates, removing rows, standardizing formats, etc. Each step shows the code, a description of what it does, and the execution result. You maintain full transparency and control.
Yes, ChartGen.ai takes data security seriously. Data is temporarily stored only during the session and automatically cleaned after processing ends. Your files are never shared with third parties. All uploads use encrypted HTTPS connections. For enterprise needs, we offer on-premise deployment options.
Absolutely! The Data Quality Checker works with any tabular data. It has been used for financial data (portfolio holdings, transactions), marketing data (CRM exports, campaign metrics), e-commerce (inventory, orders), healthcare (patient records, lab results), research (survey responses, experimental data), and more. The AI adapts its analysis to your specific data context.
After cleaning, you can export the improved dataset as Excel or CSV for use in your analysis tools. Many users then proceed to ChartGen.ai's chart generation features to visualize their clean data. The quality report can also be exported for documentation and audit purposes.
Ready to Check Your Data Quality?
Upload your file and get instant data profiling, quality scores, and cleaning recommendations.