Democratising data – from “No” to “Know”

Written by:

Peder Blomgren

Head of R&D Data Office, Data Science and AI, R&D

Mathew Woodwark

Head of R&D Data Infrastructure & Tools, Data Science and AI, R&D

Ian Dix

Head of Data and Analytics, R&D IT

Today companies like AstraZeneca are generating and have access to more data than ever before and the quantity is expected to grow exponentially.

Access to high quality data is crucial to advancing science in today’s world. Through access to connected, analytics-ready data, our scientists can uncover new insights with the aim of speeding up the discovery, development and delivery of potential new medicines to patients.

We understand how important it is for research teams to be able to rapidly access and use the data they need, in a responsible way.

So we have challenged ourselves to shift from “no” efficient access to data for reuse, to empowering our researchers to “know” what data is available and how to access it. This empowers our scientists to mine data for actionable insights that ultimately have the potential to improve patients’ lives. 

How are we doing this?

We take a cross-R&D approach to bringing the right people together to ensure we are collecting, organising and using the right data, to enable the best decision-making.

And we do all of this accordance with governing standards for protecting patient privacy and confidentiality.

It takes hard work to get data in the right shape, embed the right governance, implement the right analytics tools, and, most importantly, to get that data into the hands of the right people to yield potential transformational benefits.

We’re setting our sights high.

We aim to triple the amount of clinical data available for re-use this year. Next year, we plan to add chemistry and biologics data, imaging, multi-omics and real-world data.

Our teams are on the front lines of building the tools and infrastructure to connect rapidly-developing scientific data sources from inside and outside of AstraZeneca. Not only are we focused on process and tools, but also on R&D-wide standards for data throughout its lifecycle, including governance, policies, processes and curation – ensuring data is Findable, Accessible, Interoperable and Reusable.