Enterprise Knowledge Graph for R&D Acceleration
Scientific Innovation with FAIR Data and AI Readiness
AstraZeneca, one of the world’s leading pharmaceutical companies, has embarked on a transformative journey to make its data Findable, Accessible, Interoperable, and Reusable (FAIR), with the additional achievement of ensuring the data is ready to drive automation and AI innovation. Facing the challenges of fragmented data silos and complex regulatory requirements in precision medicine, AstraZeneca's FAIR program has set a benchmark for how life sciences organizations can harness the power of data to accelerate scientific discovery and improve patient outcomes. At the heart of this transformation in R&D lies a robust knowledge graph ecosystem, enabled by eccenca Corporate Memory, which has proven to be a critical component in realizing AstraZeneca’s visionary FAIR data strategy that supports AI innovation.
PRODUCT USED
eccenca Corporate Memory
We chose to go with eccenca corporate Memory at a graph level to manage our ecosystem. We went there because it’s a solid bit of German engineering, it’s well thought trough. What eccenca promised is that they would lower the entry level to be able to work with semantic tools and that’s exactly what they did: My engineers can now look at working on the data rather than the infrastructure.
Ben Gardner
R&D Lead for Data Mesh and Semantic Infrastructure at AstraZeneca
The Challenge – Reuse Capabilities of Legacy Systems via Fair & AI-Ready Data
Enabling Advanced Data Discovery
Advanced analytics require more than just access—they demand structured, interoperable data that can be searched and reused at scale. Knowledge graphs built on FAIR principles provide scientists and business users alike with the ability to discover relationships, patterns, and opportunities across domains, enabling smarter, data-driven outcomes.
Simplify Infrastructure Management
Legacy IT landscapes are often cluttered with custom-built tools and disconnected systems, making data governance and scalability nearly impossible. A unified semantic data layer simplifies infrastructure management, reduces technical debt, and lays the foundation for future-proof, AI-ready operations, allowing their semantic engineers to focus on rules-based data enrichment rather than maintaining complex systems.
Connect Data Enterprise-Wide
Siloed systems across departments and regions prevent organizations from achieving true enterprise data integration. Without unified access to cross-functional data, teams struggle to gain a holistic view of operations—leading to inefficiencies, missed opportunities, and inconsistent reporting.
Lowering the Barrier to Entry
When data platforms require deep technical knowledge, only a few specialists can contribute—delaying progress and limiting scalability. By simplifying access through guided interfaces and rule-based data modeling, organizations reduce dependency on IT and unlock the full potential of enterprise-wide subject matter expertise.
The Solution – From System-Centric to Data-Centric
With eccenca Corporate Memory, we transformed AstraZenecas R&D data infrastructure into a knowledge graph-driven ecosystem, ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) — and ready for automation and AI.
Together, we…
- … replaced fragile, bespoke systems with a scalable knowledge graph platform
- … standardized vocabularies and persistent identifiers (PIDs) to ensure semantic consistency
- … enabled both engineers and non-technical experts to contribute to data modeling via an intuitive interface
- … built tools like the Scientific Intelligence dashboard to execute queries in minutes instead of weeks
