Explain data minimization and how to apply it to a survey schema

WHAT THIS TESTS: The interviewer wants to see if you understand that data minimization is a proactive design principle under UK GDPR Article 5(1)(c), not a reactive cleanup task. Specifically, they are checking whether you can translate the legal standard of adequate, relevant, and limited into engineering decisions at the schema level. This means connecting privacy requirements to table structure, column definitions, and data types before any data is collected.

A GOOD ANSWER COVERS: A strong response walks through a deliberate sequence. First, define the survey's purpose precisely so you know what is actually necessary. Second, map every proposed column to a specific analytical or operational need; if a field does not directly serve that purpose, it is excluded. Third, minimize personal data by using anonymous or pseudonymous identifiers rather than collecting direct PII such as email addresses or phone numbers unless there is a strict requirement. Fourth, ensure the schema supports the principle structurally by using nullable fields only where justified, choosing tight data types to prevent over-collection, and building in a periodic review process to delete data that is no longer relevant. Fifth, demonstrate accountability by documenting the rationale for each field so the design can be audited.

COMMON WRONG ANSWERS: Red flags include suggesting you collect all possible data upfront and delete it later, which conflates minimization with storage limitation. Another error is claiming that minimization simply means having fewer rows rather than fewer columns or less invasive columns. Some candidates also mistakenly believe that hashing an email address is sufficient minimization without questioning whether the email was necessary at all.

LIKELY FOLLOW-UPS: The interviewer may ask how you would handle a stakeholder who insists on adding demographic fields for future analysis. They might also probe how you would design retention policies or how minimization interacts with the right to erasure and the right to rectification under UK GDPR.

ONE CONCRETE EXAMPLE: For a post-purchase satisfaction survey, the schema should include a pseudonymous session_id linked to the order system rather than the customer's name or email. It should store a 1-to-5 rating, an optional text comment capped at 500 characters, and a timestamp. It should not include IP addresses, device fingerprints, or marketing consent flags unless those serve a documented purpose distinct from feedback quality. A scheduled quarterly job should delete responses older than twelve months once the analysis window closes.