Rich scientific data should produce and organize actionable results that drive scientifically sound decisions. Much of that data remains entombed in IT systems that block scientific outcomes. As a result, the quality, availability and usability of existing and new data is difficult to ascertain. IBM estimates that the cost of poor-quality data, in the US alone, is $ 3.1 trillion, dramatically highlighting the need for modern research technology to enable R&D scientists engaged in a make- test- decide workflows for chemical and biological entities.
In this webinar, Dr. David Gosalvez presents use cases of improved R&D productivity and efficiency in three critical areas: Lead Discovery, Screening and capturing experimental data. See how scientists can process, find and most importantly understand critical data across R&D workflows.