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 – Legacy Systems Blocking Data Integration and Reuse
The pharmaceutical R&D processes require combining diverse data sets—from clinical trials to real-world evidence and academic collaborations—to drive insights into patient subpopulations and disease profiles. AstraZeneca’s legacy systems, built around submission-driven processes, lacked the necessary integration and reuse capabilities. These challenges were further compounded by:
- Fragmented data across many systems.
- High costs and time inefficiencies in repeated data integration.
- Limited ability to explore and query data in meaningful ways for scientists.
The need for a scalable data infrastructure - and the ability to make data FAIR and AI-ready - was clear, and they rose to the challenge.
The Solution – Moving from System-Centric to Data-Centric
Simplify Infrastructure Management
AstraZeneca replaced a mix of open-source tools and bespoke infrastructure with eccenca’s robust platform, allowing their semantic engineers to focus on rules-based data enrichment rather than maintaining complex systems.
Lowering the Barrier to Entry
A key advantage of eccenca Corporate Memory was its ability to democratize the use of semantic tools. Team members with no programming expertise were able to contribute to semantic mapping and knowledge graph creation, significantly expanding the pool of contributors with subject matter expertise. The platform’s intuitive interface and powerful data pipelining capabilities empowered AstraZeneca to accelerate the onboarding of new data sources into knowledge graphs, providing new and extended context to their data assets.
Archieving Seamless Interoperability
With eccenca’s support, AstraZeneca implemented controlled vocabularies and persistent identifiers (PIDs), embedding these into their data. This approach ensured consistent data labelling across systems, enabling seamless interoperability and reuse - a crucial step in making the data ready for AI-powered insights.
Enabling Advanced Data Discovery
AstraZeneca built their Scientific Intelligence tool atop eccenca’s knowledge graph, enabling complex queries and automated dashboards — in minutes instead of weeks. The platform’s architecture supports granular searches while respecting patient consent and privacy.
The Results – Measurable Impact: Operational Gains Through Knowledge Graphs
- Efficiency Gains: Queries that once took weeks are now completed in minutes, accelerating drug discovery timelines.
- AI-Ready Data: Standardized and interoperable data ensures compatibility with AI-driven tools, paving the way for advanced analytics and automation.
- Cost Savings: Standardized data workflows have reduced operational inefficiencies, saving both time and resources.
- Scientific Advancements: Researchers can now explore data with unprecedented granularity, driving innovation in areas like oncology and rare disease research.
- Global Collaboration: The FAIR principles implemented with eccenca’s support have fostered interoperability across internal and external stakeholders, including academic and industry partners.
