This webinar presents challenges we commonly see across global health and life sciences research teams, best practices shared across teams who thrive, and highlights one case study about an organization that has reduced friction for data scientists across the enterprise while increasing governance.
Life sciences companies require extensive collaboration across different teams and full reproducibility of data science work to meet regulation and audit needs. Models must be constantly tracked, retrained, and iterated on to reflect the constant changes that lead to model drift.
“Domino made it easier for users across the global enterprise, using different tools and with varied backgrounds and skill sets, to work with each other, leverage past work, and collaborate quickly.”
— Naveen Singla, Data Science Center of Excellence lead at Bayer
IP is the most important asset for life sciences companies, which can be at risk when key personnel leave. There's increased pressure to migrate to central, cloud-based environments that reduce key-man risk, but without sacrificing security.
Collaboration among large, distributed research teams with varying skills and tool preferences is complicated.
Dealing with sensitive PII requires regulation compliance, full visibility into project contexts, and reproducibility of past experiments.
Avoid losing valuable IP by putting everything related to life sciences research—code, data, versions, and results—into one centralized platform while using a wide range of tools and compute infrastructures.
Foster collaboration among biostatisticians, chemists, bioinformaticians, project managers, and other stakeholders to accelerate genomics research and drug discovery.
Test different scenarios through fully reproducible research. Protect sensitive proprietary data while leveraging the latest data science tools to accelerate research development and model delivery.
Four of the top 10 pharmaceutical companies and two of the five largest health insurers run their research on Domino. Domino lets research and analytics teams get insights faster, produce better risk models, meet auditing requirements, and reproduce past work. They use it for developing and testing new medical treatments, and for building and deploying API-accessible models that will drive smarter pricing, loss prevention, claims automation, customer value calculations, and more.