Which approach best describes how to handle nullability in a Clarity data model?

Which approach best describes how to handle nullability in a Clarity data model?

• A. Always allow NULLs and enforce via downstream checks
• B. Use only default values and never use NOT NULL
• C. Determine mandatory vs optional attributes, apply NOT NULL constraints where needed, provide sensible defaults, and enforce via business rules
• D. Use a deletion flag to simulate missing values

Answer: (C)

Handling nullability starts with clearly separating which attributes must always have a value from which ones can be left empty. For things that are mandatory, enforce that in the data model with constraints that prevent nulls (NOT NULL) so the system automatically rejects incomplete data rather than leaving it to downstream checks. For optional fields, prefer sensible defaults where appropriate so you avoid scattering nulls and special-case logic across the codebase; defaults keep data consistent and make queries simpler. Beyond these structural rules, use business rules to capture more complex constraints that can’t be expressed with a simple not-null or default, such as conditional requirements or domain-specific relationships. Using a deletion flag to mimic missing values tends to create confusion and complicates data and analytics. Relying on NULLs and checking later is error-prone; a well-planned mix of mandatory/not-null constraints, sensible defaults, and enforceable business rules leads to clearer data contracts and more reliable data.

Handling nullability starts with clearly separating which attributes must always have a value from which ones can be left empty. For things that are mandatory, enforce that in the data model with constraints that prevent nulls (NOT NULL) so the system automatically rejects incomplete data rather than leaving it to downstream checks. For optional fields, prefer sensible defaults where appropriate so you avoid scattering nulls and special-case logic across the codebase; defaults keep data consistent and make queries simpler. Beyond these structural rules, use business rules to capture more complex constraints that can’t be expressed with a simple not-null or default, such as conditional requirements or domain-specific relationships. Using a deletion flag to mimic missing values tends to create confusion and complicates data and analytics. Relying on NULLs and checking later is error-prone; a well-planned mix of mandatory/not-null constraints, sensible defaults, and enforceable business rules leads to clearer data contracts and more reliable data.