A project team is merging records from multiple departments and seeks to maintain accuracy across each step of a data pipeline. Which solution helps preserve data integrity throughout the transformation process?
Delete records that are missing fields to retain readable segments of data
Implement a reference that uses consistent field definitions and automated validations after each update
Focus on a final consolidation step but skip intermediate validations to speed up processing
Rely on individual departments to decide new naming patterns without a unified standard
Maintaining a shared reference for naming conventions combined with automated checks provides standardized validation and thorough reviews at every step. Other approaches can degrade data integrity. Discarding incomplete entries can lead to missing information. Relying on final-stage consolidation without intermediate scrutiny increases the chance of errors persisting. Manual reviews without referencing standard definitions lack uniform rules for evaluating data across departments.
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