Define data-cleaning quality by the decision the data must support.
The cleaning target is not a pretty table. It is data fit for a specific use. The mechanism Data quality frameworks start with fitness for use: data is good when it satisfies the requirements of the operation, analysis, or decision at hand. In cleaning, that means a blank phone number, a duplicated company, or a stale timestamp is not automatically equal in severity. The severity depends on what the data will do next. Cleaning without a use case often creates cosmetic order: columns look standardized, but the decision still fails. The mechanism is requirements first, transformations second. Name the consuming…
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