How can data quality be measured?
Data quality is not assessed in absolute terms but always in relation to a specific AI use case. What matters is whether the data is complete, correct, and representative for exactly the task the AI model is meant to solve.
Typical quality issues include incomplete data, biased or incorrect values, uneven distributions, unsuitable data volumes, ambiguous content, and non-standardized formats. Data privacy and security also play a role, especially for sensitive information.
Good data quality therefore does not mean perfect data. It means data that is structured, reliable, and functionally appropriate for the use case at hand.
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