Will enable organizations to rapidly unify and analyze diverse information for new business insights
WARREN, NJ and CAMBRIDGE, Mass.–Knowledgent, the data and analytics firm, and Tamr, the data unification solution provider, today announced a strategic partnership to deliver scalable big data unification and analytics solutions to client organizations in life sciences, financial services and other industries. As part of this partnership, the two companies will collaborate on big data analytics solutions that leverage Tamr’s scalable platform for data curation and unification, including its new Tamr for CDISC solution, being announced at Strata + Hadoop World in San Jose, Calif.
Knowledgent enables organizations to gain critical business insights from their information by combining data and analytics with industry domain expertise. Knowledgent has observed that clients struggle with the ability to connect and curate structured and unstructured data across a wide range of sources and silos, resulting in costs and delays as often manual solutions are implemented.
Tamr overcomes the traditional problems of manual data curation. It allows for continuous, cost-effective, and timely cataloging, connection and curation of data from a large number of internal or external sources with a lot of data variety. Tamr’s machine learning algorithms, guided by humans, enable organizations to maximize the effectiveness and productivity of their data assets, such as clinical trial data and financial data sets.
“Knowledgent’s data and analytics practice has been enabling clients to gain significant business insights by leveraging structured and unstructured data, internal and external to an organization,” said Tom Johnstone, Managing Partner for Life Sciences, Knowledgent. “Tamr’s platform and industry-focused solutions, such as its solution for scalable, automated CDISC data conversion, are the perfect accelerators for life sciences business and IT stakeholders who are trying to harness the power of machine learning for data curation.”
“Knowledgent is a natural partner for us,” said Andy Palmer, co-founder and CEO of Tamr. Inc. “For example, we can provide life sciences customers with a scalable, replicable way to convert siloed data to the CDISC data standard to meet data-interchange requirements for IND/NDA programs. This is a process that’s been ridiculously expensive, time-consuming and hard to automate in the past. We’re looking forward to working with Knowledgent to break down these and other business barriers for our mutual customers.”
Tamr and Knowledgent plan to deliver solutions for other use cases in life sciences and financial services that require siloed data to be converted to a standard format. An example in financial services is the BCBS 239 principles for effective risk data aggregation and risk reporting in financial services.
Knowledgent is a data and analytics firm that helps organizations transform their information into business results through data and analytics innovation. Our expertise seamlessly integrates industry experience, data analytics and science capabilities, and data architecture and engineering skills to uncover actionable insights.
Knowledgent operates in the emerging world of big data as well as in the established disciplines of enterprise data warehousing, master data management, and business analysis. We have not only the technical knowledge to deliver game-changing solutions at all phases of development, but also the business acumen to evolve data initiatives from ideation to operationalization, ensuring that organizations realize the full value of their information. For more information about Knowledgent, visit www.knowledgent.com.
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.