Predictive Analytics has emerged as a powerful tool to help answer the question “What do I test next?” However, many scientists are currently not trained to build these types of predictive models. As a result, this places the entire modeling workload on only a few data scientists, slowing response time.

New tools can help data scientists extend their expertise across the enterprise and streamline model building, allowing scientists at the bench to carry out advanced analytics “On-Demand.”

This white paper explores:

The benefits of predictive analytics in R&D
The differences between "global" and "local" models
How data science tools can speed up model building

On-Demand Analytics: Democratizing Predictive Analytics to Innovate in Biopharma