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Glossary

Data quality

How well a dataset serves the purpose it is used for. Usually judged by accuracy, completeness, consistency, validity, uniqueness, and timeliness.

Quality is relative to use. A mailing list with a few wrong phone numbers is fine for post and useless for a phone campaign.

See also

  • Data cleaningFinding and correcting values in a dataset that are wrong, inconsistent, duplicated, or missing, so the data can be relied on.
  • ValidationChecking values against rules, such as a date being in the past or a state code being on the allowed list, and reporting what fails.

Karg checks for many of these when it profiles a file, applies the fixes it is confident about, and sets the rest aside for your review. See how it works, or have our team clean your data for you.