Tamr Announces Two Packaged Data Unification Solutions for Business Analysts

Tamr for CDISC and Tamr for Procurement solve specific data-unification problems, minimizing the effort of data scientists

SAN JOSE, Calif.–Tamr, Inc., of Cambridge, Mass., today announced two data-unification solutions for specific, high-value data variety challenges: Tamr for CDISC and Tamr for Procurement. Using the solutions, business analysts can unify data across enterprise silos ─ minimizing the amount of effort by data scientists to create specific measurable value. The solutions are being introduced and demonstrated at Strata + Hadoop World in San Jose, Calif. (Booth #531).

Tamr solutions are designed to help weed through and organize enterprise data to get the most impactful data and analysis into the hands of decision-makers.

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.

“Pharma companies have been stuck on a treadmill, paying teams of contractors over and over again to prepare trial data for regulatory submissions,” said John Keilty, general manager at Third Rock Ventures and former VP of information technology and informatics at Infinity Pharmaceuticals. “This is not only expensive and time-consuming, it’s also error-prone ─ and thus very risky. Tamr’s approach replaces outdated methods of data unification and CDISC conversion with an automated, repeatable process that gets more accurate the more it’s used. Tamr could also remove barriers to early clinical data mapping, providing an opportunity for more robust data analysis.”

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.

Tamr provides a consolidated view of entities and records for any downstream application, via a set of RESTful APIs.

About Tamr, Inc.

Tamr, Inc., catalogs, connects and publishes the vast reserves of underutilized internal and external data using a combination of machine learning with human guidance so enterprises can use all their data for analytics. Tamr was founded in 2013 by big-data serial entrepreneurs Andy Palmer and Michael Stonebraker, who previously co-founded Vertica Systems (acquired by HP); Ihab Ilyas of the University of Waterloo; George Beskales; Daniel Bruckner; and Alex Pagan.