What are best practices for handling address imports with duplicates?

• A. Delete all duplicates immediately to avoid conflicts.
• B. Ignore duplicates and merge manually later.
• C. Normalize fields, apply fuzzy matching, present candidates for review, and batch-process with conflict resolution logging.
• D. Rely on external reviews to flag duplicates.

Answer: (C)

Handling address imports with duplicates works best when you clean and standardize data first, detect near-duplicates with tolerant matching, review candidates in a controlled workflow, and process changes in batches with a full audit trail. Normalizing fields means standardizing street abbreviations, casing, punctuation, and postal codes so that similar records align cleanly. Fuzzy or near-duplicate matching then spot checks records that aren’t identical but are likely the same address, even if there are small typos or format differences. Presenting candidates for review gives humans a safety net to confirm merges or keep distinct records when needed, ensuring that decisions are accurate and context is preserved. Batch-processing with conflict-resolution logging creates an efficient pipeline while keeping a trace of every decision—who approved what, when, and why—so you can audit, reproduce, or rollback if necessary.

This approach is superior because it preserves data integrity and traceability, scales to large imports, and reduces errors that can occur from manual or purely automated methods. Deleting duplicates immediately risks losing legitimate variations or important context. Ignoring duplicates leaves corrupted data in place, undermining data quality. Relying solely on external reviews is not practical for large datasets and slows down the import process.