Candidate deduplication is the detection of records in a recruiting database that represent the same person, using signals such as email address, phone number and approximate name matching. Detection surfaces suspected pairs and clusters for review. Deciding which record survives and combining them is a separate operation, usually called candidate merge.
Duplicates are created by normal behavior, not by carelessness. Someone applies to two jobs six months apart, using a personal address the first time and a work address the second. An aggregator forwards an application that already exists from a direct apply. A referral gets entered by hand while the same person is uploading a CV. Agencies submit a candidate the internal team sourced last quarter. Bulk imports from spreadsheets or a previous system arrive without the identifiers the live database matches on. Each of these routes writes a fresh record because the incoming data does not resemble anything closely enough to be recognized. Volume makes it worse: the larger the database and the more channels feeding it, the higher the chance that two entries describe one person. Detection is therefore ongoing rather than a one-off clean-up project.
Email address is the strongest single signal and the one most systems treat as an exact match. Phone number comes next, although it needs normalizing first, since the same number can arrive with a country code, spaces, brackets or a leading zero. Beyond those two, detection turns fuzzy: approximate name comparison that tolerates spelling variants and transliteration, similarity between employer and date histories, and combinations such as same surname plus same current employer plus overlapping dates. Serious systems score these signals together and apply a threshold rather than acting on any one of them alone. That threshold is the real design decision. Set it tight and detection catches only identical emails. Set it loose and it starts proposing pairs that share nothing but a common name. Reviewing a sample of what a given threshold catches is the only reliable way to tune it.
Wrongly matching is worse, and it is not close. A missed duplicate produces mild noise: inflated applicant counts, a recruiter opening the wrong record, an email sent twice. A false match puts one person's history, notes and interview feedback onto another person's profile, which is a data protection problem as well as an embarrassing one, and it is hard to unpick once anything has been combined. Common names, shared family email addresses and shared household phone numbers are where this goes wrong most often. The sensible posture is to detect broadly but act conservatively. Let the system present the records it suspects are the same, require a person to confirm anything below an exact identifier match, and never let an automated rule combine records on name similarity alone. Duplicates also distort funnel reporting, so check how your [recruitment analytics software](/recruitment-analytics-software) counts a person who exists twice.
Prevention beats detection, because every duplicate stopped at the door is one nobody has to review later. The levers are practical. Check for an existing record before creating a new one, and make that check the default path for manual entry rather than an optional step. Normalize emails and phone numbers on the way in, so trivial formatting differences do not defeat matching. Give agencies and referrers a submission route that runs the same check, since hand-typed entries bypass most controls. Clean imports before loading them, not afterwards. Where a careers site lets people apply repeatedly, tying every application to one candidate profile in your [talent acquisition software](/talent-acquisition-software) keeps a person to a single record across every requisition. None of this removes the need for periodic review, but it changes the volume from unmanageable to routine.
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