A modern data strategy gives you a comprehensive plan to manage, access, analyze, and act on data. As a result, there is a shift to consider the adoption of a data mesh architecture where data is organized by domain, clear ownership of data and technology stack is enhanced, & a more agile setup is achieved. This would enable R&D teams in clinical & pre-clinical set-ups to further enhance discovery programs by achieving faster breakthroughs.
A modern data strategy gives you a comprehensive plan to manage, access, analyze, and act on data. As a result, there is a shift to consider the adoption of a data mesh architecture where data is organized by domain, clear ownership of data and technology stack is enhanced, & a more agile setup is achieved. This would enable R&D teams in clinical & pre-clinical set-ups to further enhance discovery programs by achieving faster breakthroughs.
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.