How would you implement deduplication to detect duplicate addresses?

How would you implement deduplication to detect duplicate addresses?

• A. Ignore duplicates
• B. Normalize inputs, apply field-level fuzzy matching, consider geographic proximity
• C. Use random sampling
• D. Force unique addresses by ID only

Answer: (B)

Detecting duplicate addresses hinges on turning inconsistent address data into a consistent representation and measuring similarity across records. Start by normalizing inputs: standardize case, remove extraneous spaces, unify punctuation, expand abbreviations (St vs Street, Ave vs Avenue), and normalize suffixes and formats. This reduces the variability that can hide duplicates. Then apply field-level fuzzy matching: compare components such as street number, street name, city, state, and ZIP with similarity scores, using thresholds and tolerance for common typos or abbreviations. This helps catch near-matches where the strings aren’t exactly the same but refer to the same place. Finally, consider geographic proximity by geocoding addresses to coordinates and evaluating whether two records map to the same location or positions that are very close; near-identical coordinates strongly suggest duplicates. When used together, normalization, fuzzy field comparisons, and spatial proximity form a robust deduplication approach that can tolerate formatting differences and minor inaccuracies. Why the other options fall short: ignoring duplicates leaves redundant data that can skew results and metrics; random sampling may miss duplicates entirely and isn’t a reliable method for deduplication; forcing uniqueness by ID only assumes every record already has a perfect, consistent ID and ignores cases where different IDs still refer to the same address.

Detecting duplicate addresses hinges on turning inconsistent address data into a consistent representation and measuring similarity across records. Start by normalizing inputs: standardize case, remove extraneous spaces, unify punctuation, expand abbreviations (St vs Street, Ave vs Avenue), and normalize suffixes and formats. This reduces the variability that can hide duplicates. Then apply field-level fuzzy matching: compare components such as street number, street name, city, state, and ZIP with similarity scores, using thresholds and tolerance for common typos or abbreviations. This helps catch near-matches where the strings aren’t exactly the same but refer to the same place. Finally, consider geographic proximity by geocoding addresses to coordinates and evaluating whether two records map to the same location or positions that are very close; near-identical coordinates strongly suggest duplicates. When used together, normalization, fuzzy field comparisons, and spatial proximity form a robust deduplication approach that can tolerate formatting differences and minor inaccuracies.

Why the other options fall short: ignoring duplicates leaves redundant data that can skew results and metrics; random sampling may miss duplicates entirely and isn’t a reliable method for deduplication; forcing uniqueness by ID only assumes every record already has a perfect, consistent ID and ignores cases where different IDs still refer to the same address.