Enterprise Entity Resolution That Scales with You

Entity resolution is a core data quality process used to identify records that refer to the same entity within or across data sources. This could be done for deduplication and cleansing purposes, or to enrich and create golden records that absorb entity fragments across your business and create a unified entity profile.

Data matching is the process of comparing records to determine whether they refer to the same thing (a person, company, product, or location). Entity resolution is the broader workflow that uses data matching plus standardization, scoring, review, and consolidation to create a trusted, unified view of that entity across datasets. Identity resolution is a specific type of entity resolution focused on people or customers—connecting identities across systems, channels, and identifiers to build a single customer view.

Data Ladder reduces false positives by combining data standardization with configurable match rules and match scoring, so records aren’t merged based on a single weak signal. Teams can weight stronger identifiers more heavily (like emails or phone numbers when available), use multi-field logic (name + address + phone), and define thresholds to separate clear matches from “possible matches” that require review. This approach helps maximize true matches while minimizing incorrect merges.

DataMatch Enterprise supports multiple matching approaches depending on data quality and use case. Deterministic matching uses exact rules (for example, a consistent ID or email match). Fuzzy matching compares similarity for fields like names and addresses to handle typos and variations. Probabilistic-style scoring combines multiple attributes into an overall confidence score so records can be matched even when no single identifier is perfectly reliable. These methods can be used together to improve accuracy across real-world datasets.

When multiple records represent the same entity, survivorship rules determine which values become the “best version” in the golden record. For example, you can prioritize a trusted source system, prefer the most recent value, keep the most complete field, or select a standardized format. Survivorship ensures the golden record is not just a merge—it’s a controlled, consistent representation of the entity that supports downstream systems, analytics, and operations.

Accordion ContentYes. Data Ladder can resolve entities across multiple systems by ingesting records from different sources, standardizing key fields, and applying matching logic to identify which records represent the same real-world entity. This supports common enterprise scenarios such as unifying customer data across CRM and billing systems, consolidating vendor records across ERP instances, or deduplicating and resolving entities before data is published to a warehouse for analytics.