To ensure data is accurate and complete before beginning analysis, which data validation technique is most commonly used?

Schema validation is crucial in data validation as it checks that each field in a dataset adheres to the expected structure, format, and constraints. For instance, schema validation can confirm that date fields are consistently formatted and that numerical fields contain appropriate values. This helps prevent errors in downstream analysis by catching issues early in the data pipeline. Schema validation is essential for data integrity, especially when data is sourced from multiple systems, ensuring that all fields align with expected specifications. The other options are incorrect because: • Option 1 (range checking) is part of validation but doesn’t address structural consistency. • Option 2 (outlier analysis) helps identify abnormal values but is not a structural validation method. • Option 4 (removing duplicates) cleans data but does not validate structural consistency. • Option 5 (aggregating data) summarizes data rather than validating it, making it unrelated to schema accuracy.