Entity resolution

Deduplication cleans one list; entity resolution connects your whole stack. A tradeshow's data lives in fragments: registration platform, event app, badge scans, CRM, exhibitor lead captures, past editions' archives. The same person appears in all of them — as maria.lopez@acme.com in registration, m.lopez@gmail.com in last year's data, and "Maria L., Acme" in an exhibitor's scan list. Entity resolution stitches those fragments into one identity, which is what makes real questions answerable: has this attendee come three years running? Did this exhibitor's leads actually convert to next year's registrations? Is this "new" prospect actually a lapsed customer? Modern approaches score the probability that records match — same name plus same company plus overlapping phone number — and increasingly use AI to handle the messy middle where rules fall short. For organizers, resolution is the foundation under loyalty analysis, lifetime value, and any honest cross-edition reporting; without it, every returning attendee looks brand new. The honest nuance: resolution is probabilistic, so decide your tolerance per use case. For marketing segmentation, a rare wrong link is acceptable. For consent, billing, or anything legal, only high-confidence matches should merge — and the resolution logic itself needs documenting, because "the system linked them" isn't an answer regulators or customers accept.