How is your organisation using data and analytics to support the corporate vision and purpose?
At AstraZeneca R&D, we harness data and technology to maximise time for the discovery and delivery of potential new medicines. We use data and analytics to drive our decision-making and to make us more efficient. For decision-making, this can mean which potential medicines to progress, which diseases to tackle, how best to get our medicines to the patients who need them, or which other organisations to collaborate with. For efficiency, this can mean automation of manual processes through computational solutions, streamlining our supply chain or ensuring that we maximise R&D productivity.
2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?
2020 really was unprecedented and impacted life as we know it both personally and professionally. From having to lead the team remotely through uncertainty, learning new ways to engage and bring people together, to pivoting to support AstraZeneca’s comprehensive response to the pandemic, it really has been a year like no other. My group was able to launch a suite of projects aimed at supporting the company’s response to the pandemic: from using public data to model pandemic progression, to investigating novel AI on medical images for COVID diagnosis, to providing design and analysis support for our vaccine programme.
Looking forward to 2021, what are your expectations for data and analytics within your organisation?
I believe that data, analytics and AI have the potential to transform the way we discover and develop new medicines and we are only at the tip of the iceberg in terms of its potential. In 2021, I expect us to continue to realise more and more value directly in our R&D pipeline, through data and analytics. And I expect us to continue to put data at the heart of the key decisions we make when discovering and developing innovative new medicines.
Is data for good part of your personal or business agenda for 2021? If so, what form will it take?
We recently published our principles on ethical data & AI and intend to continue our focus on how this weaves in to all aspects of how we utilise data in our business through 2021. We wanted to be pro-active in ensuring our approach is aligned with our sustainability commitments and company values. One of those five principles is that our use of data and AI should be “human-centric and socially beneficial”.
What has been your path to power?
I started life as a high energy particle physicist, pondering the subtle differences between matter and anti-matter, and how they might affect the universe. This saw me acting as a “data scientist” working on big data many years before those terms were coined.
After completing a post-doctoral fellowship in the field, I went into consultancy with Tessella, which specialised in scientific software engineering and analytics. This broadened my horizons across a range of sectors, including petrochemicals, consumer goods, robotics and life sciences.
I then moved to AstraZeneca in 2007, keen to take my rich skillset of mathematics, computing and science and apply it in an area which can directly benefit people’s health and wellbeing. Initially, I was a biomedical informatics scientist and then quickly took on positions of increasing leadership and strategic responsibility. First was leading the biomedical informatics team globally, then from 2012 assuming a senior role leading a new department called the Advanced Analytics Centre in AstraZeneca R&D.
Since 2019, I have been vice president of data science and AI, an R&D-wide role leading end-to-end capabilities from data governance and standards, through tools and infrastructure, to AI and modelling.
What is the proudest achievement of your career to date?
In short, it is being appointed to my latest role. I have always been driven by the potential to provide more value for patients, by having an ever-wider strategic impact on the power of data to transform lives. I couldn’t be prouder, feel more privileged or more humbled than to be in my current position which is the job of a lifetime.
Tell us about a career goal or a purpose for your organisation that you are pursuing?
Overall, our goal is to see data & AI transform the discovery and development of innovative new medicines. This can happen in two ways: 1) we use AI to significantly accelerate our R&D cycles, getting medicines to patients that need them sooner; 2) we use AI to enhance our decision-making so that we become more accurate in choosing which programmes to take forward, based on predicting their success up front.
How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?
Within my organisation, data and analytics are very closely aligned to our business processes and are used to support decision-making as well as to measure and streamline processes. Across industry sectors more broadly, the picture is more variable, but the clear trend is to more data-driven organisations in the future.
The convergence of the two - business with data and analytics - I see as driven by two primary factors: 1) a gradual up-skilling of the workforce to appreciate better the potential opportunities and risks that data and analytics hold for them; 2) a trend with data tools and applications to be more self-serve and for use by a much broader and varied user base.
What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?
There are a number of aspects to this, so let me focus on one - know your audience. Particularly in a large organisation, there are not just lots of individuals, but also multiple “personas”. For instance, senior executives, middle managers, data scientists, allied data professionals, lab scientists. Ensure that you have your personas framed and detailed appropriately for your organisation and build your data change initiative around them. My organisation includes a dedicated R&D data science learning and development team focused on just this approach.