Get a Data Quality Score
for Any CSV in Seconds
Upload a CSV and get a weighted 0โ100 score across completeness, uniqueness, type consistency and statistical integrity plus type mismatches and 3-sigma outliers with exact row numbers, an inferred schema, and a downloadable JSON report.
Drop your CSV file here
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Supports any CSV regardless of size. No row limit.
How to Check Data Quality Before Loading into Power BI or a Database
Skipping a quality check is the leading cause of dashboards showing wrong numbers, database imports failing with cryptic errors, and reports that a stakeholder later flags as inconsistent with the source system.
What Makes Up Your Data Quality Score
The score starts at 100. Each of the four factors below can deduct points up to its weight, and the final score is max(0, round(100 โ total deductions)). A score of 90+ is Excellent (ready for production), 70โ89 is Good (minor issues to review), and below 70 is Poor (multiple problems that will cause errors downstream).
Completeness
40%Deduction = (100 โ overall fill rate) ร 0.4. A dataset with 80% overall fill rate loses 8 points.
Uniqueness
30%Deduction = duplicate row % ร 1.5, capped at 30. Reaches the cap once 20% of rows are duplicates.
Type Consistency
20%7 points lost for every column where the dominant type (number/date/boolean) has exceptions, capped at 20.
Statistical Integrity
10%3 points lost for every numeric column containing values more than 3ฯ from the mean, capped at 10.
Type Mismatches and Outliers Issues Other Tools Miss
Duplicate rows and missing values are well-known problems with dedicated tools. These two checks are different they only surface when you analyse the shape of the data within each column, and they are the most common cause of "the data looks fine but the dashboard is wrong."
Type Mismatches
This tool first determines the dominant type of each column number, date, boolean or text by checking what percentage of non-empty values match each pattern (numbers handle commas, currency symbols and percent signs; dates match common YYYY-MM-DD and DD/MM/YYYY formats). If a column is at least 70% one type, every value that does not match is flagged as a mismatch with its row number. An "amount" column with 997 numeric rows and 3 rows containing "N/A" or "pending" will have those 3 rows listed exactly. In Power BI, a single non-numeric value forces the entire column to be typed as text, silently breaking every SUM and AVERAGE. In SQL, the import simply fails.
Statistical Outliers (3-Sigma Rule)
For every column classified as numeric, the tool calculates the mean and standard deviation of its values (excluding any type-mismatch rows), then flags any value more than three standard deviations from the mean the standard threshold used in statistical process control. A "quantity" column averaging 12 with a standard deviation of 3 would flag a row containing 9,999 as an outlier roughly 3,329ฯ away. Outliers are not automatically wrong, but a single typo like an extra zero can shift an average enough to mislead an entire report, and this check surfaces exactly which row to verify.
Frequently Asked Questions
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