A data analyst is exploring a new dataset containing customer order information. The dataset includes an 'order_date' column and a 'ship_date' column. During the initial review, the analyst needs to check for records where the 'ship_date' is logically incorrect, for example, occurring before the 'order_date'. Which data validation technique is specifically used to identify this type of inconsistency?
Completeness check, which identifies records where date values are missing.
Cross-field validation, which checks for logical relationships between values in different columns.
Format validation, which ensures dates are stored in a consistent structure like 'YYYY-MM-DD'.
Range validation, which verifies that date values fall within an expected time period.
The correct answer is cross-field validation. This technique involves checking for logical consistency and relationships between the values of multiple fields within the same record. In this scenario, it would verify that the 'ship_date' is on or after the 'order_date', as it is logically impossible to ship an item before it has been ordered.
Range validation is incorrect because it checks if a value in a single column falls within a predefined, static range (e.g., ensuring 'order_date' is after the company's founding date), not its relationship to another column's value.
Format validation is incorrect because it only ensures that the data is stored in a consistent structure (e.g., 'YYYY-MM-DD'). It would not catch a logical error between two correctly formatted dates.
Completeness check is incorrect because it only verifies that a field is not empty (i.e., does not contain a null value). It does not evaluate the correctness of the data that is present.
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