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Tamr launched at DataBeat in May 2014 via a presentation by co-founder Andy Palmer. Tamr connects and enriches the vast reserves of underutilized internal and external data, allowing enterprises to use all their data for analytics and decision making. Tamr combines machine learning and advanced algorithms with collective human insight to identify data sources, understand relationships and curate siloed data at scale. Leverage All Data Organizations have vast, untapped reserves of structured and semi-structured data from internal and external sources. Instead of using just a portion of available data, Tamr lets customers analyze any and all of their sources. As Tamr connects more sources, it gets better, improving accuracy and speed. And Tamr works smoothly with the data tools and experts customers have already invested in. A Growing Data Challenge The cost and complexity of preparing the massive variety of internal and external data to power analytics and applications are unacceptably high. As a result, most organizations use less than 10% of the relevant data available. Tamr dramatically reduces the time and effort required to connect and enrich data sources, allowing data scientists, analysts and managers to focus on data-driven innovation. The Tamr Solution Tamr starts by analyzing data sources—tens, hundreds, thousands—applying advanced algorithms and machine learning to connect and curate 90% or more of attributes and records. To improve precision, it taps experts with the best knowledge or insight on particular sources. This unique collaborative approach allows Tamr to speed up data connection and preparation and to provide enterprise scalability. Big Benefits in Little Time Tamr unleashes the power of 100% of your data. One Tamr customer has connected more than 15,000 data sources in a single view. Another that struggled with slow, manual curation saw Tamr automatically curate 80% of attributes. And a third saw Tamr finish a connecting and enrichment project in 2 weeks that typically takes 6 months.
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tamr
New tech is great, but the quality and connectedness of enterprise data often sucks
the dirty data secret
scientific freedom
Good for research creativity, bad for data connectivity
scientific freedom
Good for research creativity, bad for data connectivity
the integrated view Collaborative R&D through open data sharing
Good for research creativity, bad for data connectivity
the integrated view Collaborative R&D through open data sharing
the source challengeFifteen thousand strong…and in need of a new approach
scientific freedom
top down integrationNeat, clean…
Neat, clean…and relatively inflexible
top down integration
Neat, clean…and relatively inflexible
The Choice:Ignore itOr start all over!
The Consequences: Missed opportunity Ballooning costs
top down integration
An exponential challenge
the missing capability
Connecting and curating in an automated way
semi-structured data: JSON sources
Embrace the reality of data variety across the entire enterprise
bottom-up curation
Probabilistic approach as primary design pattern — some semantic web mojo
the time has come
1990’s web:probabilistic search and website connection!
2020’s enterprise:probabilistic data source connection & curation
back to the future
Can we remove the ceiling on the number of data sources that can be dynamically integrated?
hypothesis
NEA
®
early production results
15K sources integrated into one view
Tamr unified view
early production results
Over 90% reduction in manual reviews
Records
90%reduction
Unique
Manual ReviewMatched
3% to manually review
Proprietary
Tamr
!key design point!
• Continuous bottom-up/ probabilistic approach Combination of Machine Learning and Expert SourcingIntegrated data and metadata through APIs
•
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NEA
®
