Self-Serve Entity Resolution Pipeline Reveals Three Key Lessons

A self-serve entity resolution (ER) system was built and evaluated on six benchmarks ranging from 864 to 5 million records. The study yielded three lessons absent from existing ER literature. First, no single matching algorithm performs best across all datasets; a self-serve pipeline cannot predict its next dataset, so training several algorithm families per dataset and using an automatic bake-off to select the winner is recommended. Second, precision and recall require separate fixes: precision needs hard rule-based vetoes, while recall benefits from more diverse candidate retrieval. Third, a single false-positive link can silently merge unrelated entities because assuming transitivity (A matches B, B matches C implies A matches C) allows one bad link to chain hundreds of records together; therefore, every cross-group merge must be actively re-verified. The authors hope these lessons save practitioners months of dead-end experiments.