80%+ less manual effort
Dramatically reduce time and resources required to develop and maintain a mastering pipeline compared to a rules-based approach.
Complete, trusted data
Machine learning-based approach identifies relationships that are almost impossible to find manually, enabling much higher levels of accuracy across all your data.
Scales to the challenge
Modern, API-oriented architecture scales up and down to meet the volume demands of each entity, whether it’s 10s of thousands or 100s of millions of records.
Ready to learn more? Connect with an expert today.
Leverage structured and semi-structured data
Mastering model trained by human guided ‘yes / no’ feedback
Golden records provide source of truth for each entity
Enrich source data
Reduce the time to analytic outcomes
“Boil the ocean” MDM projects can take years to demonstrate value. Tamr’s agile, machine learning-based approach collapses the cycle time between business question and analytic outcome by making it possible to get unified views of key entities in days.
Learn how Littelfuse achieved new insights into their customer base in under 3 weeks.
Capture the value of all of your enterprise data
Avoid the time, expense, and headache of purchasing multiple data mastering products to integrate data to support initiatives across domains. Tamr is capable of mastering all enterprise entities, and through the use of machine learning, can cost-effectively unify the long tail of data sources.
See how Toyota gained a first-ever, complete view of its customers.
Improve your entire ecosystem
Our system embraces the heterogeneity of technology through an API-oriented architecture that operates as ‘table in / table out’, allowing you to design your data pipelines in a way that is optimized for your current stack.
Learn how GSK transformed their data environment into a best-of-breed ecosystem.