CDISC Conversion

CDISC Conversion

4 of the Top 5 Pharma Companies Use Tamr

Clinical Data Conversion (CDISC): Simpler, scalable conversion

Tamr’s CDISC solution offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards. By understanding SAS input and output formats, in addition to controlled terminologies, Tamr combines machine learning and human guidance to automatically convert your clinical trial dataset to a specific standard.For most pharmaceutical companies, submitting clinical study data to the FDA is an expensive and time-consuming process. A study sponsor must collect data from different sources, extract it from proprietary file formats, transform it to conform to CDISC standards, organize metadata describing these transformations, execute validation scripts to ensure data consistency, and convert it all into the file formats required by the FDA for submission.

Typically, the arduous task of aggregating, transforming, and validating this data falls to teams of contractors or employees using proprietary software and guided by complex conversion standards. When standards are updated or if a submission contains an error, the entire conversion must be restarted.

Tamr’s CDISC solutions offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards. By understanding SAS input and output formats, in addition to controlled terminologies, Tamr combines machine learning and human guidance to automatically convert your clinical trial dataset to a specific standard.

Benefits:

  • Automatically convert data from the most popular proprietary formats
  • Machine learning + human guidance maps study data to a target CDISC standard
  • Programmatically engage people who generated the data to answer questions

read the datasheet Schedule Demo

Tamr’s CDISC solutions offers a dramatically simpler and scalable way to automatically convert, validate, and package clinical study data in file formats organized according to the latest CDISC standards.

Biting The Data Management Bullet At GlaxoSmithKline

“There comes a time in the life of almost every large organization when it has to admit that it doesn’t have the data environment it needs to succeed.” That’s how Tom Davenport and Randy Bean’s article in Forbes begins. Their story describes how GSK’s Chief Data Officer has led an effort to transform the company’s data management capabilities to accelerate its digital transformation. Tamr’s machine learning-based enterprise data unification platform has been a core component of GSK’s initiative.

“GSK R&D’s data environment is something that one often hears about in startups, but is rarely found in large enterprises.”

– Tom Davenport – Author, Speaker, Advisor

Read the Article in Forbes

 

How Amgen Built a Translational Data Platform at Scale

Learn about Amgen’s journey to build a translational data platform at scale through innovation of its data management practices – including the use of human-guided machine learning to automate the incorporation of hundreds of legacy datasets. By reducing the traditional roadblocks to integrating data (cost, complexity, and time), Amgen has been able to exponentially speed up the time it takes to unify data sources across its organization – ultimately leading to expedited R&D decision making and rapid new hypothesis generation.

Tamr’s machine learning capabilities were the key reason we selected this software to integrate different sources into a bridge based datahub.” – Jackie Fu, Specialist IS Business System Analyst, Amgen