After running automated data-quality checks on a customer table, an analyst notices a metric showing that 137 of the 12,000 records violated at least one validation rule. Which data-quality metric would provide the analyst with that specific count so they can review the problematic rows?
Rows failed reports the number of individual records that did not satisfy at least one validation rule, allowing analysts to isolate and correct bad data. Conformity measures the count of data points that meet the defined rules, while non-conformity tallies data points (not necessarily whole rows) that violate rules. Rows passed indicates how many rows cleared every validation rule and therefore does not highlight problem records.
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What are validation rules in data quality checks?
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How is 'Rows examined' different from 'Rows failed'?
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Why is 'Rows failed' important in data quality monitoring?