Equifax Identity Matching vs Precisely Data Matching and Entity Resolution
Equifax Identity Matching
developer.equifax.comPrecisely Data Matching and Entity Resolution
precisely.comSide by side
| Figure | Equifax Identity Matching | Precisely Data Matching and Entity Resolution |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Identity Resolution Software | 8.0 (#7) | 7.2 (#19) |
| Founded | 1899 | 1968 |
| Headquarters | Atlanta, Georgia, United States | Burlington, Massachusetts, USA |
| Integrations | Batch services are supported through MFT integrations, and the listed cloud environments are Snowflake and Google Cloud Platform BigQuery. | Precisely’s partner ecosystem includes AWS, Azure, Google Cloud, Cloudera, Confluent, Databricks, Snowflake, SAP, ServiceNow, and Splunk. |
| Intended users | Equifax identifies financial services, telecommunications, utilities, and other consumer service providers as suitable industries. | The solution is positioned for organizations seeking trusted entity data for decision-making, customer engagement, fraud detection, anti-money-laundering, and compliance use cases. |
| Purpose | Identity Matching identifies and links customer information across disconnected data sources, including when names or addresses vary. | Connects records across systems that refer to the same real-world entity to create a trusted view of customers, suppliers, assets, and other entities. |
| Security | Equifax says Identity Matching data undergoes encryption and tokenization to support data privacy and compliance controls. | Precisely says its SaaS solutions are assessed to SOC 2 Type II annually and certified to ISO 27001:2022. |
| Use cases | Equifax lists CRM improvement, identifying unique customers, customer retention through targeting and personalization, and reducing data integration and collection costs as use cases. | The maker lists customer 360, anti-money laundering, fraud detection, and confident business decisions as use cases. |
Plans
Precisely Data Matching and Entity Resolution
- Data matching and entity resolution solutionsPriced on request
In their own words
Equifax Identity Matching
Equifax Identity Matching links customer information held in disconnected sources, including records where names or addresses differ. It assigns persistent consumer identifiers and can create consumer, household, and address keys, helping organizations form a single customer view. Equifax says it compares multiple datasets and data-element combinations, and can combine company records with consumer credit and specialty finance data. Matching and updates are described as cloud-based and near real time, with incremental key changes available through delta services. Delivery options include batch services, an online API, and cloud-native Keying as a Service in Snowflake or Google Cloud Platform BigQuery environments. Confidential Keying keeps raw personal information in a customer cloud environment. Data is encrypted and tokenized, usage is regulated by GLBA, and processes are regularly validated. OAuth 2.0 client credentials secure APIs; production access requires credentials and whitelisted IP addresses. Pricing is on request.
Precisely Data Matching and Entity Resolution
Precisely Data Matching and Entity Resolution links records across systems when they refer to the same real-world entity. It is designed to create trusted views of people, households, businesses, suppliers, assets, products, parts, and locations for uses such as customer 360, fraud detection, anti-money laundering, and business decisions. Configurable matching and AI/ML support work across large, complex datasets with less dependence on IT. Precisely solutions can profile data, apply business rules, and enrich records with demographic, firmographic, and location intelligence before matching. Spectrum Quality provides phonetic, distance-based, and country-specific algorithms for B2B and B2C names across more than 200 countries. The solutions work in on-premises and cloud environments; listed platforms include Linux, Windows, web, and self-hosted. Batch file import, household matching, and organization matching are supported. Precisely reports that Schwan’s experienced a 25% decrease in new customer duplicate data. Pricing is available on request. The company was founded in 1968 and is headquartered in Burlington, Massachusetts, USA.