During a monthly review, a team discovers that several records fail to meet established data standards. Which approach helps resolve this non-conformity while keeping the integrity of the dataset?
Replace the invalid fields with standard placeholders
Remove the problematic rows from the dataset
Implement an automated process to tag and restructure the records
Sign off on the existing data without correcting the records
Applying an automated process to designate and fix data that fails to meet the standards preserves accuracy and completeness. Removing them causes data loss. Authorizing them in their current state leaves inaccuracies. Replacing them with defaults diminishes the quality of the stored information.
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What types of automated processes can be used to tag and restructure records?
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Why is it important to maintain the integrity of the dataset when correcting non-conformities?
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What are the consequences of not addressing non-conformities in data records?