Tamr Data Quality is a cloud-based, AI-native platform designed to improve the quality, completeness, and reliability of enterprise data. It automates the validation, standardization, and enrichment of data from multiple sources, providing organizations with accurate, up-to-date, and consolidated “golden records.” The platform leverages pre-integrated APIs, built-in reference sources, and machine learning models to resolve entities, monitor data quality metrics, and streamline error handling. Tamr’s solution is suitable for organizations seeking to eliminate manual data wrangling, reduce engineering overhead, and ensure data consistency for operational and analytical use cases.

Key Features

Pre-integrated APIs and Reference Sources Tamr provides out-of-the-box integrations with external data providers and reference datasets.

• Eliminates custom connector development
• Accelerates onboarding of new data sources

Automated Data Validation and Standardization The platform validates and standardizes key attributes such as addresses, phone numbers, and company names.

• Ensures data consistency and accuracy
• Reduces manual data cleansing efforts

AI-driven Entity Resolution Tamr uses machine learning to resolve duplicate and related records across disparate sources.

• Creates consolidated “golden records”
• Reduces data duplication and fragmentation

Data Quality Monitoring and Insights Built-in dashboards and metrics track data quality issues and improvements over time.

• Monitors error rates, completeness, and values fixed
• Supports ongoing data stewardship and curation

Data Enrichment Enhances internal datasets with supplemental information from reputable external sources.

• Adds missing demographic, firmographic, or geographic details
• Improves data completeness and utility

Maintenance and Service Error Handling Managed performance, error handling, and version control.

• Reduces engineering and operational costs
• Ensures reliable and up-to-date data processing

Benefits

Trusted, High-Quality Data Ensures organizations have access to accurate, complete, and up-to-date information.

• Improves decision-making and operational efficiency
• Reduces risks associated with poor data quality

Reduced Engineering Overhead Automates data quality tasks and error handling.

• Frees up technical resources for higher-value projects
• Minimizes manual intervention and maintenance

Faster Time to Value Accelerates data onboarding and consolidation.

• Enables rapid deployment of data products
• Supports agile business initiatives