Deduplication is the step that catches the same contact or event showing up twice from different sources, run before enrichment, scoring, or a triggered send so a team doesn't process a record it already has.
Raw signal and contact feeds double-count constantly. The same job change can show up from a LinkedIn scrape and a data provider's own alert on the same day. The same person can enter a list twice under two slightly different emails, or once from a form fill and again from an enrichment pass that found them independently. None of that is unusual. It's the default state of a stack pulling from more than one source, which is most stacks.
Dedupe has to run before anything downstream touches the record, not as a cleanup pass after the fact. Skip it and a team enriches and scores the same event twice, burning credits proving something it already knew. Run it late instead of early and the cost shows up twice before anyone notices the duplicate: once in wasted enrichment spend, and again when a rep works the same lead under two different names without realizing it's one account.
Without it, the damage compounds quietly elsewhere in the stack. Contact records duplicated three different ways is a common symptom of a broken sourcing or list-import process, and once records like that land in a CRM, no manual cleanup inside the tool fixes the upstream habit that keeps producing more of them. Dedupe is what stops that habit from reaching the record in the first place.
Put dedupe as its own explicit step in the pipeline, ordered before enrichment and before any ICP or fit filter runs. A duplicate that slips past this point gets enriched, scored, and possibly sequenced twice, which costs more than the dedupe check itself would have.
Dedupe gets treated as a one-time list-cleaning task instead of a step every new record has to pass through. The other mistake is trusting a single field, usually email, as the only match key, when the same person can show up under two different addresses or through two different signal sources entirely.
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