Bad data leads to bad decisions. A single column with 40% missing values can skew your entire analysis. Duplicate rows can double-count revenue. Type mismatches can break calculations. A data health check catches these problems before they cost you.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. Even for individuals and small teams, bad data means:
The fix is simple: check your data quality before you analyze it.
Queryly's data health check scans your dataset for five categories of issues:
Shows which columns have missing data and what percentage. A column with 50% missing values probably shouldn't be used for analysis without imputation. The health check tells you exactly where the gaps are.
Identifies data points that are significantly different from the rest. An average salary of $50,000 with one value at $5,000,000 will skew your results. Outlier detection flags these before they distort your analysis.
Detects when columns have inconsistent data types. A "price" column that contains both numbers and text strings. A "date" column with some entries as "2026-01-15" and others as "January 15, 2026". These mismatches break calculations.
Finds rows that are exact or near-exact duplicates. Duplicate rows inflate counts, skew averages, and can make your dataset look larger than it actually is. The health check tells you how many duplicates exist and which columns to check.
Flags problems like constant columns (every value is the same), high-cardinality columns (too many unique values to be useful), and columns with suspicious patterns that might indicate data entry errors.
The health check runs in seconds. You get a health score (0-100) and a detailed breakdown of every issue found.
If the health check finds issues, Queryly can help fix them. Upload the same file and ask:
The AI writes and runs the cleaning code automatically. You download a clean version of your data.
Run a Health Check Now