Tamr Cloud is a fully managed SaaS solution that uses an AI-native approach to master data management (MDM). Tamr Cloud uses AI in unique and proven ways to efficiently solve challenging data quality problems at scale.

Tamr Cloud enables you to solve these analytical and operational challenges:

• Maintaining a centralized index for master data across multiple systems.
• Providing your organization with the single, authoritative, and accurate version of a business entity’s data across multiple data sources and datasets (the golden record).
• Ensuring your golden records are accurate through ongoing human review and curation.
• Operationalizing your data pipeline.
• Enabling real-time data operations against a System of Record (SOR), including search, create, and update.

Quickly clean, master, and enrich your data using configurable, entity-specific data products. Each data product includes these key features:

• Industry-specific schemas for companies, healthcare providers, business contacts, customers, and so on.
• Built-in data quality and enrichment services to ensure your data is as accurate and complete as possible.
• Fully trained AI/ML models to deduplicate your source records.
• Golden record logic to create a single, authoritative, and accurate version of each business entity across all data sources.
• A persistent Tamr ID that links clustered source records to their associated golden record.
• Support for ongoing human review and curation.

Use your data products to power analytics, data visualizations, business intelligence, and other tools used by your organization.

Tamr Cloud also supports operational use cases. With Tamr Cloud RealTime, you can search for, create, and update records in real-time, directly in the Tamr RealTime System of Record (SOR). See About Tamr RealTime for more information.

Tamr's Approach Versus Rules-Based Mastering

Tamr's AI-drive approach to MDM offers many advantages over traditional rules-based data mastering, including the following:

Rules-Driven Approaches	Tamr Cloud Approaches
Developer-created rules that require constant updates as data changes.	Pre-built, purpose-trained machine learning models.
Closed systems aimed at selling a broader suite of products.	Interoperability with existing applications across the data value chain.
High cost for adding new sources requires significant preprocessing and modification.	Easily onboard new sources by leveraging purpose-trained machine learning models and pre-defined data mastering flows.

The Tamr ID

During the mastering process, each entity and its clustered records is assigned a Tamr ID, which is a unique, persistent identifier. Tamr automatically manages the Tamr ID throughout the mastering process to ensure that it remains a unique string.

You can use thes IDs to join records across datasets and maintain consistency.

See Requirements for Source Datasets for more information about the importance of maintaining stable primary keys to retain persistent Tamr IDs.