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Data Quality Checker Score Any Dataset in Seconds

Get a complete data quality report with a score from 0 to 100. Detects nulls, duplicates, type mismatches and outliers with auto-generated insights explaining exactly what each issue means and why it matters.

🏆 Quality score 0–100
💡 Auto insights
🔧 One-click fixes
📄 Export report JSON
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CSV & Excel Data Analyzer

Find duplicates, nulls & errors · Clean & export · Auto dashboard

∞ All Rows🔒 No Upload⚡ Free
🔁 Duplicates⬜ Nulls⚡ Type Check📊 Stats📉 Outliers💡 Insights🔧 Clean📈 Dashboard⬇️ Export📡 API
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Drop your data file here

or click to browse CSV, Excel (.xlsx / .xls) or JSON

CSVXLSXXLSJSON

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Free CSV and Excel Data Analyzer Find Duplicates, Nulls and Errors Instantly

Upload any CSV or Excel file to instantly find duplicate rows, null values, type mismatches and data quality issues. The Clean Data tab lets you remove duplicates, fill nulls and standardize headers in one click, then download the cleaned file. The Dashboard tab auto-generates charts from your data. No Python, no SQL, no formulas required.

How do I find and remove duplicate rows in CSV?
Upload your file, then go to the Clean Data tab. Click "Remove Duplicate Rows" to deduplicate instantly. The row count updates and you can download the cleaned file as CSV, Excel or JSON.
Can I fill null values instead of deleting rows?
Yes. In the Clean Data tab, choose "Fill Nulls" with options to fill with 0, with the column mean, or with an empty string. This lets you keep all rows while fixing missing values.
What does the Dashboard tab show?
The Dashboard tab auto-generates bar charts for numeric columns showing value distribution, and donut charts for categorical columns showing the most frequent values. It gives you an instant visual overview without any configuration.
Does this tool process all rows?
Yes. There is no row limit. All rows are processed in your browser. Your files never leave your device.

What the Data Quality Score Measures

The quality score is a single number from 0 to 100 that summarises the overall health of your dataset. It is calculated from four factors. Column completeness how full each column is, where 100% means no nulls and anything below 70% heavily penalises the score. Type consistency whether numeric columns contain only numbers or have text mixed in. Data uniqueness whether duplicate rows are inflating the dataset. And statistical integrity whether numeric columns have outliers that suggest data entry errors.

90–100
Excellent

Clean and ready for production use, database import or dashboard connection.

70–89
Good

Minor issues exist. Review the insights and fix before using in production reports.

0–69
Poor

Multiple significant problems. Fix before analysis or the results will be unreliable.

How to Check Data Quality Before Loading into Power BI or a Database

The most common scenario where data quality checking prevents serious problems is before loading a CSV or Excel export into a BI tool or database. Skipping this step is the leading cause of dashboards that show wrong numbers, database imports that fail with cryptic errors, and reports that a stakeholder later flags as inconsistent with source system data.

1
Upload your export file to this tool
2
Check the quality score and read the auto-generated insights
3
Go to the Issues tab and review each detected problem
4
Use the Clean Data tab to fix issues: remove duplicates, fill nulls, standardize headers
5
Download the cleaned file and import it into Power BI or your database

Frequently Asked Questions

How is the data quality score calculated?
The score starts at 100 and deductions are applied for each quality issue. Missing values reduce the score based on how many columns are affected and how incomplete they are. Type mismatches deduct points per column with inconsistent types. Duplicate rows deduct up to 30 points depending on the percentage of duplicates. The final score is capped at 0 and rounded to the nearest integer.
What is the difference between this tool and Excel data validation?
Excel data validation prevents bad data from being entered in the first place. This tool analyzes data that already exists and finds problems. It is most useful when you receive data from an external system, a client, or an automated export and need to check its quality before using it.
Can this tool validate data against rules I define?
The current version uses automatic detection based on statistical analysis. It detects type patterns, null rates and outliers without requiring you to define rules. Custom rule-based validation (such as "amount must be positive" or "email must contain @") is not currently supported.
Can I use this to check API data quality before building a dashboard?
Yes. Switch to the API tab, enter your REST endpoint URL, and optionally add a Bearer token for authenticated APIs. The tool fetches the JSON response and runs the same quality check as for files. This is useful for checking the quality of live API data before connecting it to a dashboard.

Related Data Tools

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Focused tool for detecting and removing duplicate rows
CSV Null Value Checker
Find and fix missing values column by column
Excel Data Analyzer
Full analysis for Excel files with the same quality score
Find Outliers in Data
Detect statistical outliers with 3-sigma analysis