Like many organisations AstraZeneca R&D faces the challenge of siloed data. We see adopting Findable, Accessible, Interoperable, Re-usable (FAIR) Data principles as a route to releasing value from our existing data as well as setting us up to be able to do so much more with new data we generate from here on. Semantic knowledge graphs are a proven approach to achieving this and we started by building Scientific Intelligence, a knowledge graph to support exploration and analysis of clinical data.
Traditionally building knowledge graphs has focused on combining inconsistent, siloed data from multiple sources into a common data model, the graph. This requires developing a data model consisting of entities, the relationships between them and the attributes that describe them. Having created the model data from sources is then mapped into it and transformations created to align the data to a common set of standards. This is a non-trivial exercise often made harder by the lack of metadata to describe either the source or the individual fields within. As a result a huge amount of effort is spent understanding and fixing other people's data. As a consequence, capacity to scale the breadth and depth of the knowledge graph rapidly becomes limited. The Scientific Intelligence knowledge graph was adding significant business value but continuing to grow it would require more semantic engineering resources. Given the shortage of this skill set we had to think differently. The challenge was clear, how do we get everyone else to fix their data so we could focus on the semantics and the knowledge graph. The answer was to turn the problem on its head and move from putting the data into the graph to putting the graph into the data. This pivot now forms the core of data standards, interoperability and governance strategy for AstraZeneca R&D. In this presentation I will discuss this journey, the lessons learnt and describe the simple pragmatic services we have put in place to enable easy adoption and compliance.