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Informatica Alternative

Lower cost of ownership, higher data accuracy, and insights obtained in weeks—not years.

Discover the benefits of  Tamr's cloud-native Master Data Management (MDM) Approach vs Traditional MDM

How CAA Super-charges Insights with Data Mastering

Learn how CAA accelerates insights into client opportunities by taking a human-guided machine learning approach to the data mastering.

See how our customers accelerate business outcomes with Tamr


Four Reasons for Switching from Traditional MDM


Lower cost of ownership of data mastering projects leveraging cloud-native capabilities


Reduced manual workflows by up to 90% and free up the time of highly-skilled data teams


Accelerated data projects to weeks or months instead of years


Seamless integration with data management stack  a robust basis for modern DataOps infrustructure

Cloud-Native Data Mastering at Scale: The MDM Alternative

Tamr provides an effective approach to MDM and ETL:

Machine learning does the heavy lifting to consolidate, cleanse and categorize data, enabling teams to drive better business outcomes faster. 


Tamr Data Mastering vs Informatica MDM

A side-by-side comparison



Results delivered for large-scale data mastering projects in days to weeks

Results delivered for large-scale MDM projects in months to years


Reduces the time to add new data sources by up to 90%

Months of iterations with IT to integrate new datasets

Cloud Support

Only cloud-native data mastering solution

Deployed on-prem or VM in the cloud


Patented ML algorithms compare millions of records in minutes

Rules logic take months to develop for limited datasets

Data Accuracy

Studies show 90%+ accuracy with Tamr human-guided ML

Rules-based systems typically produce 50-80% accuracy


RESTful APIs and modern data publishing capabilities

Low interoperability with data pipeline and technology stacks


Highly adaptive models can readily master data across multiple domains

New business domains require completely separate configurations and deployment efforts


ML models drastically reduces manual maintenance cost and cloud-native capabilities significantly lower hosting costs

Costly to develop, maintain, and operate rules logic, while on-prem and VM deployments drive significant hosting costs



A US Financial Institution estimates ~$20M in annual savings from deploying Tamr for one data mastering project.


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