Which validation checks should run on data imports into OIMS?

Which validation checks should run on data imports into OIMS?

• A. Field length, mandatory fields, data type checks, cross-reference lookups, duplicate detection, date validity
• B. Field length only
• C. Mandatory fields only
• D. Data type checks and date validity

Answer: (A)

Validating data during import needs a multi-layered approach to keep the system reliable. Field length protects against truncation and overflow, ensuring values fit the database schema. Mandatory fields guarantee essential information is present so records aren’t incomplete. Data type checks confirm that values match the expected types (numbers, dates, strings), which prevents downstream processing errors. Cross-reference lookups verify codes or IDs against reference data, catching typos or invalid references. Duplicate detection stops importing the same record multiple times or colliding with existing data, preserving uniqueness. Date validity ensures the dates are real calendar dates and sensible for the context. If any one of these areas is skipped, different issues can slip through—like orphaned references, missing critical data, or invalid dates—leading to broader data integrity problems. That combination of checks covers all the common failure points, making it the best choice.

Validating data during import needs a multi-layered approach to keep the system reliable. Field length protects against truncation and overflow, ensuring values fit the database schema. Mandatory fields guarantee essential information is present so records aren’t incomplete. Data type checks confirm that values match the expected types (numbers, dates, strings), which prevents downstream processing errors. Cross-reference lookups verify codes or IDs against reference data, catching typos or invalid references. Duplicate detection stops importing the same record multiple times or colliding with existing data, preserving uniqueness. Date validity ensures the dates are real calendar dates and sensible for the context. If any one of these areas is skipped, different issues can slip through—like orphaned references, missing critical data, or invalid dates—leading to broader data integrity problems. That combination of checks covers all the common failure points, making it the best choice.