Tamr Enterprise Platform for Scalable, End-to-End Data Unification

The new Tamr Platform radically simplifies and speeds the availability of unified data for analytics and downstream application, with key new features: catalog, connect, and consume. Tamr also announced solutions for Pharma and Procurement.

Feb 17, 2015. Tamr, a Cambridge, MA Big Data startup, introduced today at Strata + Hadoop World San Jose a new version of its scalable data unification platform. The company also announced the addition of GE, Roche and Toyota as customers.

Tamr uses machine learning and smart social engineering to dramatically improve the process of data preparation and data unification, usually the most time-consuming part of data warehousing and data mining.

The new Tamr Platform simplifies and speeds the process to make unified data available for analytics and downstream applications. The key new features are catalog, connect, and consume.

Tamr Catalog, Connect, and Consume

Catalog: A Central Inventory of Enterprise Metadata

Tamr Catalog is a new tool for automatically cataloging in a central, platform-neutral place all metadata available to the enterprise. The decision makers can now view an inventory of all of the company's metadata organized by the logical business entities, versus only 20% to 30% of metadata enumerated based on how they are stored.

Connect: Easier Data Unification across Data Silos

Improvements in scalable data connection simplify and improve data unification, enabling it to be applied to more kinds of data and business problems. Improvements include easier matching of multiple entities by taking into account relationships between them; and enhanced support for Oracle, Hadoop, unstructured data, and user-defined functions via tools such as OpenRefine, Springbok and Data Wrangler. Data analysts and data curators — both seasoned and less-experienced — benefit.

Consume: Access and Enrich Data via Google Sheets or Excel

Anyone can enrich spreadsheets or other applications with unified data from across the enterprise. Via new APIs and a new spreadsheet plug-in, analysts can find, map and match external data with their internal data in Google Sheets or Excel. Tamr handles the mechanics of data matching and enrichment, prompting users with suggestions and then auto-populating their spreadsheets with new and changed data based on their choices. By being able to manipulate external data as easily as if it were their own, business analysts can use the data to resolve ambiguities, fill in gaps, enrich their data with additional columns and fields, and more.

Tamr also announced Two Packaged Data-Unification Solutions for Business Analysts :

Tamr for CDISC provides a simple, scalable way to automatically convert, validate, and package clinical study data according to the latest CDISC standards. Today, organizing CDISC data submission is expensive, time-consuming and difficult to automate. Companies typically spend millions of dollars annually on specialty contractors, who manually convert the data from the proprietary-system files in which it's stored. Tamr understands the systems' input and output formats and controlled terminologies, and automatically converts clinical datasets using the proper definition files. Future transformations become easier as Tamr learns, enabling businesses to build in-house conversion and integration expertise.

Tamr Procurement Tamr for Procurement enables a comprehensive analysis of procurement opportunities that leverages data across all enterprise systems. Today, procurement data is spread across siloed ERP and supply chain systems, making it hard to do a comprehensive analysis of savings opportunities. Tamr provides a simplified, unified view of supplier, part and site transaction data across the enterprise. It achieves this by (1) referencing each transaction and record across many data sources, (2) building correct supplier names, addresses, ID's, etc. for a variety of analytics, and (3) cataloging an organized inventory of sources, entities and attributes. Customers can now find all sourcing opportunities, including "long-tail" opportunities that can often add up to 75% or more of total savings.

Patent-pending technology using machine learning algorithms performs most of the work, unifying up to 90% of different entities. When human intervention is necessary, Tamr generates questions for data experts, aggregates responses, and feeds them back into the system. This feedback enables Tamr to continuously improve its accuracy and speed.

For more information, visit tamr.com.