Fill in the blank: Data _____ is a cleaning feature to check the accuracy and quality of data before adding or importing it.

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Data validation is the process of checking data against rules to ensure accuracy and quality before it’s added or imported. It involves confirming data types, ensuring required fields are present, verifying values fall within allowed ranges, checking formats (like emails or dates), and maintaining referential integrity. In a data pipeline, validation acts as a gate at load time, so records that don’t meet the rules are rejected or flagged for correction, preventing bad data from entering the dataset. While data cleaning, transformation, and enrichment are useful for improving data, validation specifically targets ensuring data conforms to defined quality and schema criteria before loading. For example, validating a new customer record would ensure the email has a proper format, required fields are present, and IDs aren’t missing or obviously invalid.