Tamr offers software and services to help large enterprises accelerate and scale their data driven projects. Tamr applies machine learning supplemented by customers’ expert knowledge to automate the unification of large numbers of data silos at a fraction of the time and cost of alternative approaches. When enterprise data sources are full of dirty, redundant, and noisy data Tamr can clean and unify the data. The end result is a clean, accurate, master set of data. Tamr is used by enterprises for a wide range of applications, including spend optimization, single view of a customer, biopharmaceutical data integration and regulatory compliance. Based on patented software from Turing Award winner Michael Stonebraker, Tamr’s software enables customers to transform their approach to data unification to uncover new analytic insights. Companies like GE, Toyota, Thomson Reuters, Huawei, GSK and others have achieved transformational outcomes by partnering with Tamr

What's new

  • Tamr is a new offering through HPE Complete

Features

The Connect Phase

Project Goals are defined and identification of the entities (e.g. person, place or thing) the user wants a unified view of for the purpose of downstream analysis

Tamr aligns all relevant source dataset attributes to a unified schema that is most effective and relevant for project goals

Human-guided machine learning is employed to union these datasets and offers a significant improvement in speed and scale as compared to traditional methods that rely on writing scripts

The Clean Phase

Deduplicating and mastering the entities within the unified dataset efficiently and accurately through the use of human-guided machine learning

The issue of dirty, duplicative data across enterprise data systems is extremely common and a one that is very difficult to solve using conventional data management techniques

The principal function of this phase of Tamr is record matching and de-duplication, powered by Tamr’s machine-driven, human-guided approach

The Classification Phase

Once a clean, unified dataset of a particular entity has been produced by Tamr, the user has the option of “classifying” the records to a company-specific or commonly used taxonomy for more in-depth analytic capabilities downstream

This is particularly true within use cases such as supply chain or procurement analytics - where taxonomies help organize entities into logical groupings for business and analytic purposes

Tamr’s classify phase operates in the same manner as the connecting and cleaning phases