Developing and Implementing a Scientific Data Strategy for Pharma
Recorded On: 02/05/2018
The discovery research paradigm requires integration of a broad range of human biology data and knowledge in order to generate and explore diverse hypotheses. Scientists often spend a significant amount of their time and resources in analytics and informatics projects trying to find, access, understand, curate and integrate data. While scientific information is generally managed effectively for its primary use, it often lacks the accessibility and context that facilitates secondary use and cross-functional integration on-demand. As a result, much of the research informatics efforts across the pharmaceutical industry are focused on creating single point solutions to these challenges within a particular problem space or functional area. As the use of predictive modeling, analytics and machine learning increases to address the challenges of declining R&D productivity and increasing pressures for demonstrating product value, a cohesive scientific data strategy and scalable approaches are required to handle the ever increasing variety of data types, data sources, data models and analytics patterns. It also calls for a reevaluation of data access rules, accountability, and data stewardship culture to realize business strategic goals while managing risk.
Nicole is currently a director in Merck's Scientific Information Management organization. She is an epidemiologist by training and began her career in academia conducting large-scale observational research studies before joining Merck. She now leads the Scientific Data Development team at Merck, responsible for defining and executing a data strategy to improve the utility of Merck’s scientific information across the company’s drug development pipeline through data-centric, analytics-focused solutions.