The primary issue is Uniqueness because more than one record represents the same real-world entity. Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.

Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.

DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.

The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.

The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Uniqueness; Matching; De-duplication; Chapter 10 — Master Data Management; Entity Resolution.

===============