Are disparate data silos the bottleneck for your digital transformation and artificial intelligence (AI) initiatives?
Tamr accelerates data-driven initiatives by automating the amalgamation of disparate data silos. This allows the user to master hundreds of data sources significantly faster and more reliably than legacy approaches.
The Tamr platform blends machine learning, rules, and a patented human-in-the-loop workflow for schema unification, record matching, and classification. This unique combination efficiently solves the data variety challenge while giving customers more control over the accuracy and quality of its data. It enables large organizations to address their data debt and create a new class of data assets that accelerate their digital transformation and AI initiatives.
Tamr is backed by National Enterprise Associates (NEA), Google™ Ventures, and the corporate venturing arms of several of leading enterprises like Thomson Reuters, GE, Samsung, HPE, and MassMutual.
- Tamr platform can be licensed directly from Hewlett Packard Enterprise
- The platform ships with the modern Big Data components needed to master data at scale - Spark, HBase, Elastic, Postgres
Get Results in a Fraction of the Time of Manual, Rules-based Approaches
Tamr, available through HPE Complete, provides a human-guided, machine-driven workflow to substantially accelerate data mastering initiatives.
The Tamr solution can reduce new source onboarding from six or more months to less than a week.
Cost-effectively onboard all available data and gain a holistic view of key business entities.
Scale Up to Hundreds of Millions of Records
Tamr’s API-oriented architecture built on leading-edge components enables seamless integration into existing data pipelines along with the scalability and resilience needed for the most complex enterprises.
Scale data mastering efforts to gain complete visibility of activity with each business entity.
Equip End-users with Accurate, Complete Data
Tamr continuously learns from feedback and reports metrics that provide full transparency into the effectiveness of its models.
Customers have complete control over how data is mastered, creating confidence in the end-results and helping improve adoption of data and analytics.
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