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.
- Developing a platform that provides a curated, integrated data hub with advanced search and analytic tools to accelerate translational research & decision making
- Utilizing machine learning-based approaches to automate the integration of both current and legacy clinical study datasets
- Ensuring the highest levels of accuracy in unified data through the use of internal validation coupled with automation
Steven Hobmann, University of California, Santa Barbara
Principal IS Business System Analyst, Amgen – R&D Department / Amgen
Steve is an experienced IT leader that currently leads development of a knowledge management platform for Amgen R&D to accelerate drug development pipeline by enabling fast decision making using advanced analytics and big data integration technologies.
Jackie Fu, Wayne State University
Specialist IS Business System Analyst – R&D Department / Amgen
Jackie is a seasoned IT specialist with deep domain knowledge in clinical data and data harmonization. Prior to joining Amgen, Jackie worked at automotive companies and research institutes developing analytical tools and marketing applications.
Ted Snyder, Bowdoin College
Senior Solution Architect – Field Engineering Department / Tamr
Ted Snyder is a senior solution architect at Tamr, where he works with customers in the pharma and biotech industries. Before Ted joined Tamr, he was the lead senior informatics analyst at Infinity Pharmaceuticals where he oversaw Infinity’s clinical data warehouse, including mapping of clinical trial data to the CDISC SDTM model, and helped end-users across clinical development visualize and interpret their data. His background includes experience in software development and data visualization. He earned a BA in biochemistry from Bowdoin College and was a contributing author to “A Picture is Worth a Thousand Tables: Graphics in Life Sciences” (Springer 2012).