We are in a data-driven era of R&D, where many pharma companies focus more on acquiring and integrating high-quality datasets rather than generating new data.

This track explores innovative new ways for how data is captured, structured, governed, secured and shared to support compliance, collaboration and scalable AI-driven innovation across the pipeline.

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11:30
  1. Biopharma Data Management Stage
    30 mins
    The vast majority of sequenced genes are functionally uncharacterized , and this “functional dark matter” continues to grow exponentially with metagenomic sequencing. Addressing this challenge require …
12:00
  1. Biopharma Data Management Stage
    30 mins
12:30
  1. Biopharma Data Management Stage
    30 mins
    How generative AI can support the full lifecycle of analytics work—not just dashboard creation, but also review, documentation, interpretation, and decision-making.  Common failure points in real-worl …
14:40
  1. Biopharma Data Management Stage
    30 mins
    Curation, movement, and management of unstructured 'omics data is difficult but necessary to drive biological insights  Collection and harmonization of scientific, instrument, and clinical metadata is …
  2. Biopharma Data Management Stage
    30 mins
    Reserved for TTEC Digital
16:10
  1. Biopharma Data Management Stage
    30 mins
    The compendium of information related to genes and genomes across platforms and model organisms require alignment of the myriad gene symbols, reference genomes, clinical associations and molecular clu …
16:40
  1. Biopharma Data Management Stage
    30 mins
17:10
  1. Biopharma Data Management Stage
    30 mins
    Examine the growing integration of large language models and knowledge graphs in life sciences, highlighting real-world applications and synergies.  Debate the optimal balance between AI automation an …
11:30
  1. Biopharma Data Management Stage
    30 mins
    Eisai’s in-house developed Target Gene Notebook (TGN) exemplifies the dual-use platform, which provides a conduit between subject matter experts and their audiences, while also streamlining the expert …
12:00
  1. Biopharma Data Management Stage
    30 mins
12:30
  1. Biopharma Data Management Stage
    30 mins
    Key levers where data can be used to increase pipeline throughput and reduce cost  How we capture and augment business processes to improve efficiency and make data reusable  Case-study: Near-real-tim …
14:00
  1. Biopharma Data Management Stage
    30 mins
    From Chaos to Control: How Takeda built a regulatory data backbone strong enough for GenAI—without compromising compliance.  Data as a Product: Why defining domains, CDEs, and ownership councils is th …
14:30
  1. Biopharma Data Management Stage
    30 mins
    Data collection architecture: integrating instruments, custom apps/API + ELN to capture operational + scientific metadata at the source and ensure end-to-end sample traceability.  Analysis at scale: o …
15:00
  1. Biopharma Data Management Stage
    30 mins
    Clinical Data Management: definition, scope, current state of the industry  Differences and similarities in CDM across different organizations (pharmaceuticals, biotechnology, medical devices, Contrac …