Precisely Data Matching and Entity Resolution
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VisitPrecisely Data Matching and Entity Resolution vs Zingg
What both publish
| Fact | Precisely Data Matching and Entity Resolution | Zingg |
|---|---|---|
| Entity coverage | Configurable matching supports people, households, businesses, suppliers, assets, products, parts, and locations. | The product is positioned for customers, suppliers, products, locations and other entity types. |
| Headquarters | Burlington, Massachusetts, USA | Lewes, Delaware, United States |
| Integrations | Precisely’s partner ecosystem includes AWS, Azure, Google Cloud, Cloudera, Confluent, Databricks, Snowflake, SAP, ServiceNow, and Splunk. | Documented connectors include Databricks, Snowflake, Cassandra, S3, Azure, Elastic, Exasol, relational databases and Spark-supported sources. |
| Intended users | The solution is positioned for organizations seeking trusted entity data for decision-making, customer engagement, fraud detection, anti-money-laundering, and compliance use cases. | The maker describes Zingg as a tool for analytics engineers and data scientists to integrate data silos and build unified views at scale. |
| Purpose | Connects records across systems that refer to the same real-world entity to create a trusted view of customers, suppliers, assets, and other entities. | Zingg is open source, warehouse native master data management software for resolving entities and building unified views from siloed data. |
| Support | Precisely offers customer support plans, including 24/7 severity support, and a portal with documentation, downloads, API information, and forums. | The comparison page lists open source support for Community and enterprise grade support for both Enterprise editions. |
Precisely Data Matching and Entity Resolution plans
- Data matching and entity resolution solutionsPriced on request
Zingg plans
- CommunityFree
- Community (Open Source)Free
- EnterprisePriced on request
- Enterprise LitePriced on request
- Zingg EnterprisePriced on request
- Zingg Enterprise LitePriced on request
Pick Precisely Data Matching and Entity Resolution if…
This solution suits organizations seeking to reconcile entity records across large datasets for customer, supplier, asset, fraud, or compliance work. It may also fit teams that need configurable matching across on-premises and cloud environments.
Pick Zingg if…
You want what its maker publishes above — check its page for the details.