AWS Entity Resolution launches advanced matching using Levenshtein, Cosine, and Soundex

Today, AWS Entity Resolution announces advanced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help organizations resolve consumer records across fragmented, inconsistent, and often incomplete datasets. This feature introduces tolerance for variations and typos, enabling potentially more accurate and flexible entity resolution without requiring the manual pre-processing of records. Advanced rule-based fuzzy matching in AWS Entity Resolution helps customers improve match rates, enhance personalization, and unify consumer views, critical for effective cross-channel targeting, retargeting, and measurement.

AWS Entity Resolution advanced rule-based fuzzy matching bridges the gap between traditional rule-based and machine learning-based matching techniques. Customers can use fuzzy algorithms to set similarity, distance, and phonetic thresholds on string fields to match records, offering the configurability of deterministic matching with the flexibility of probabilistic matching. This feature can be applied across multiple industries including advertising and marketing, retail and consumer goods, or financial services, where resolving consumer records are critical for verifying customers, fraud detection, or marketing purposes.

AWS Entity Resolution helps organizations match, link, and enhance related customer, product, business, or healthcare records stored across multiple applications, channels, and data stores. You can get started in minutes using matching workflows that are flexible, scalable, and can seamlessly connect to your existing applications, without requiring any expertise in entity resolution or ML. AWS Entity Resolution is generally available in these AWS Regions. To learn more, visit AWS Entity Resolution.