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Tuesday 26 January 2027 | 10:00 – 16:00
*** This session is not open to everyone. It is an invitation only event ***
Create an environment in which scientific data can remain interpretable, accessible and valuable over time
Biopharma organisations generate enormous volumes of scientific data across discovery, translational research, clinical development and real-world evidence. Yet much of that data remains fragmented across applications, functions, partners and geographies, making it difficult to discover, connect and reuse beyond the project or system that originally generated it.
As R&D becomes increasingly data-intensive, and organisations look to deploy AI across scientific workflows, this fragmentation is becoming a more fundamental constraint. The challenge is no longer simply how to store more data, but how to create an environment in which scientific data can remain interpretable, accessible and valuable over time.
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10:30 Session 1: From Application-Centric Data to Reusable Scientific Data Products
Core question: How do we design scientific data so that it remains useful beyond the experiment, project or application that originally created it?
- What minimum metadata, lineage, provenance and identifiers are required to make scientific data genuinely reusable?
- How can organisations capture sufficient context at the point of generation without creating unsustainable additional work for scientists?
- How should teams balance immediate scientific needs with designing data for future and secondary use?
- How should organisations decide which historical datasets are worth curating and which are no longer worth the investment?
Paidi Creed, Vice President of Engineering, Relation Therapeutics
11:30 Session 2 : Semantics, Standards and Interoperability
Core question: How can organisations create enough common meaning across scientific data to make it reliably interoperable without over-standardising research?
- What does semantic interoperability actually look like in practice, and where are ontologies, semantic layers and knowledge graphs creating measurable value?
- What minimum level of shared terminology, identifiers and standards is necessary to connect data across R&D?
- How can organisations avoid creating internally consistent but externally incompatible "FAIR silos"?
- Where could pharma collaborate pre-competitively on terminology, identifiers and semantic models rather than repeatedly solving the same problems independently?
13:30 Session 3: Connecting Distributed Scientific Data
Core question: How do we make distributed scientific data discoverable and accessible across R&D without needing to centralise everything?
- Which scientific data should be centralised, and which should remain within specialist systems?
- How can organisations connect ELNs, LIMS, omics platforms, clinical systems and external datasets without creating another layer of fragmentation?
- What architectural approaches are proving practical for connecting distributed data, including APIs, data contracts, semantic layers and federated models?
- How should organisations provide access to data that cannot easily move because of privacy, sovereignty, regulatory or contractual restrictions?
Marisa Cunha, Product Owner, Enterprise AI, AstraZeneca
14:30 Session 4: Operating Models for R&D Data
Core question: Who should own, govern and maintain R&D data capabilities across the organisation?
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What should be owned centrally, and what should remain within scientific, therapeutic-area or specialist data teams?
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Who should be accountable for data quality, metadata, stewardship, shared standards and reusable data products?
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Who owns and maintains a successful data capability after the original project, pilot or funding programme ends?
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How should organisations fund and maintain shared data capabilities when the costs sit centrally but the benefits are distributed across multiple teams?
15:30 Closing Session: From Discussion to Action
The final session will bring the day's discussion together through a short round-the-room reflection.
Each participant will be asked:
What is the one R&D data capability your organisation most needs to improve over the next 12 months?
The session will identify shared priorities across organisations and highlight where there may be opportunities for continued peer learning or pre-competitive collaboration.