400 Scientists Share Secrets to Maximizing R&D Data

Data are more important than ever in today's challenging R&D environment, but as most lab directors, research directors, and senior strategy leaders will attest, strategic use of R&D data can be as challenging as it is promising.Take, for example, the opportunity that artificial intelligence and machine learning (AI/ML) present for organizations to leverage decades of proprietary R&D data to help guide and accelerate new initiatives.Experts tasked with bringing an AI/ML vision to fruition know that success hinges on first addressing a myriad of challenges, from identifying realistic use cases for applying AI/ML within the current research environment to readying the multimodal data needed to train models.While this type of groundwork is not often fodder for headlines, it is essential to success.A similar narrative can be told about almost any opportunity of strategically using R&D data.It is never simply a matter of making a decision and flipping a switch.Rather, maximizing the impact of R&D data involves an incredible amount of work behind the scenes to first establish the precursory data infrastructure needed to capture and process vast volumes of diverse data being generated by teams using many different tools and technologies.This often necessitates sourcing new technologies and trusted partners to help establish strong data processes from the earliest steps of data collection and automation through to querying, modeling, and analysis.