Data deduplication is the process of finding records that represent the same person, company, product, or other entity, then removing redundant records or merging their useful values.

The records do not need to be identical. These rows can describe the same contact while containing complementary or conflicting data:

text
jane@example.com | Jane Carter | (empty)

jane@example.com | J. Carter   | +1 415 555 0199

Data deduplication example: keep the information, remove the extra row

For the two contact rows above, matching on jane@example.com produces one candidate group. If you confirm that both rows describe Jane Carter, a useful merged result is:

EmailNamePhone
jane@example.comJane Carter+1 415 555 0199

The phone fills an empty field. The different names require a choice: keep the full name after review rather than selecting a row at random. If the records also contain conflicting job titles or two CRM IDs, decide which values belong in the result before deleting either source row. Keep a backup and the source IDs when another system still references them.

Deleting the second row without merging would lose the phone number. Merging all matching email addresses blindly can also be wrong: a shared mailbox such as sales@example.com may represent several people. Choose a match key that fits the entity you want to keep.

Record deduplication versus storage deduplication

For a CSV, spreadsheet, or CRM, deduplication compares records and resolves which rows and values to retain. In file storage or backups, storage deduplication can mean storing repeated data blocks once to save space. That storage operation does not decide which customer record has the correct phone number. This page covers record deduplication.

Deduplication has two separate decisions

First, decide whether records match. Then decide what to do with the group.

1. Find duplicate records

Choose properties that identify one record and configure how they are compared:

  • Exact matching for IDs, codes, and already normalized values.
  • Smart matching for harmless formatting differences in emails, URLs, phone numbers, text, and company names.
  • Distance matching for spelling differences and typos.
  • Phonetic matching for names that may sound alike.

Several properties can reduce false matches, but requiring too many values can hide duplicates when one field is empty. See multi-column deduplication for this tradeoff.

2. Process each duplicate group

After matching, you can:

  • Remove extra rows when they add no useful information.
  • Merge complementary values into one record.
  • Resolve conflicting values with a field rule or manual review.
  • Skip uncertain groups.

⚠️ Similar does not always mean duplicate

A fuzzy or phonetic match creates a candidate group. Review weak identifiers such as names before deleting or merging records.

Deduplication within or across lists

Single-list deduplication finds duplicate records inside one table. Cross-list deduplication compares separate files or collections, often to remove contacts from an incoming list when they already exist in a CRM.

Read cross-list deduplication for the distinction.

Datablist's Duplicates Finder supports Exact and Smart matching on the Free plan, advanced fuzzy and phonetic algorithms on paid plans, group review, removal, merge previews, and conflict resolution.

Follow the tested list deduplication workflow, or open the Duplicate Finder reference for every algorithm and setting.