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
Speed
Results delivered for large-scale data mastering projects in days to weeks
Results delivered for large-scale MDM projects in months to years
Agility
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
Scalability
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
Interoperability
RESTful APIs and modern data publishing capabilities
Low interoperability with data pipeline and technology stacks
Multi-Domain
Highly adaptive models can readily master data across multiple domains
New business domains require completely separate configurations and deployment efforts
Cost
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.
