During routine data quality checks, a retail company is reviewing its sales database to improve record completeness. Several order records lack essential fields such as customer email and product SKU. Which control will most effectively ensure each new record contains all required information before it is committed to the database?
Use encryption tools to safeguard critical records and important information data points
Implement an automated process that compares fields to a standard reference and blocks incomplete records from being stored
Sandbox incomplete records in a separate location until after data analysis
Allow each user to decide which fields to fill with no validation on required data fields
The most reliable approach is to use automated validation rules that compare incoming data to required-field standards and block records that fail those rules. By stopping incomplete records at the point of entry, the organization prevents gaps in critical information and maintains overall data completeness. Approaches that leave validation to individual users, postpone checks until after analysis, or focus solely on encryption do not systematically fill missing fields in time to protect data quality.
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Why is an automated process better for ensuring data completeness compared to manual checks?
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What is a 'standard reference' and how does it help in validating database fields?
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How does blocking incomplete records from being stored improve data quality?