Webinar

[Fireside Chat] Data Management & Automations for Enterprise Data in Drug Discovery

Key Highlights

Today, therapeutics & pharma companies performing R&D are generating data at breakneck speed which has led to the digitization & incorporation of AI & ML initiatives across organizations. This is to ensure that the data is discoverable, easily stored & used for secondary research, in other words - data is FAIR. Join Reenu from Beam Therapeutics & Manimala from Elucidata over a fireside chat over the benefits & nuances that digital transformation means to the pharma companies & how it has helped to strategize the value of the data generated.

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[Fireside Chat] Data Management & Automations for Enterprise Data in Drug Discovery

What the AI Co-Scientist Paper Actually Demonstrates for Biologists and Data Scientists

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Real-World Applications We’ll Cover

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%.

What You’ll Learn

Why This Matters for Biomedical Researchers

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.

Meet the Experts of this discussion

Key Takeaways

How data providers ensure adherence to quality standards through validation and compliance.

How GUI-based workflows, CLI tools, and collaborative workspaces enable streamlined data ingestion and synchronization at scale.

Understand how automated pipelines assess conformance, plausibility, and consistency, ensuring high-quality, AI-ready data products.

Key Takeaways

Reduce operational costs by streamlining data delivery through reusable, governed products.

Accelerate diagnostic development and clinical trial execution by delivering compliant, high-quality data at scale.

Improve audit readiness and regulatory confidence through governed data products and built-in quality assurance.

Equip cross-functional teams to act on trusted data—faster, and with greater confidence.

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Other Resources

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