Data Quality Framework
Standardize your data quality process. Master the 6 dimensions of data quality, source-specific validation, ETL checks, and compliance. Reduce cleaning time from 80% to 20%.
Private by design
For file-based tools, processing is designed to happen in your browser. Avoid uploading confidential files to any website unless you are comfortable with the workflow and have permission to use the data.
Use responsibly
Use the result as a practical first pass, then verify any important decision with the appropriate source or professional.
Free access
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What does this tool check?
The tool runs 10 automated checks on your CSV and produces a prioritised list of data quality issues, from critical errors down to informational notes.
The 10 checks explained
Duplicate rows: Identical rows that will skew aggregations and counts.
Missing values: Null or empty cells per column, shown as a percentage.
Mixed data types: Columns where some rows are numbers and others are text — usually data entry errors.
Inconsistent casing: Columns with "London", "london", and "LONDON" treated as different values.
Leading / trailing whitespace: Invisible spaces that cause joins and lookups to fail silently.
Outliers: Numeric values more than 3 standard deviations from the mean.
Constant columns: Columns with only one unique value — usually safe to drop before modelling.
High cardinality: Columns with nearly all unique values — likely ID or free-text fields.
Date columns stored as text: String columns that look like dates but are not typed as dates.
Very long strings: String values over 500 characters — possible merged cells or encoding errors.