What are best practices for handling address imports with duplicates?

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 build a clean, auditable workflow that catches near-duplicates before they’re merged. Start by normalizing fields so addresses share a consistent format: standardize street suffixes (Street vs St vs ST), abbreviations, casing, and unit representations, and unify ZIP or postal codes. This reduces false differences that come from formatting rather than actual data.

Next, apply fuzzy matching to detect near-duplicates across components like street address, city, and ZIP. By measuring similarity rather than requiring exact matches, you can identify addresses that are essentially the same but have minor typos, abbreviations, or variations. This creates a set of candidate matches with confidence scores for review.

Then present those candidates for human review rather than automating every merge. A reviewer can confirm, reject, or adjust which record should be the canonical one, ensuring accuracy and handling edge cases that automated rules might miss. This step balances speed with correctness and provides a clear UI for decisions and rationale.

Finally, batch-process with conflict resolution logging. Process imports in manageable chunks, apply consistent merge rules, and log every decision—what was merged into what, who approved it, and when. This creates an auditable trail, supports rollback if needed, and keeps the import scalable for large datasets.

Other approaches fall short because deleting duplicates immediately can throw away legitimate data; ignoring duplicates and merging later delays resolution and can leave messy data during the import; and relying on external reviews alone isn’t practical for large, time-sensitive batches.