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Ian Dix

Ian Dix, Head of The Enterprise Data Office, AstraZeneca

Describe your career to date


My data and AI career, spanning over 25 years, has been deeply embedded in the pharmaceutical data and analytics field. Starting in academia in the 1990s, as a postdoctoral in genetics analysing of large volumes of genetic experimental data, I evolved from a ‘wet biologist’ to a ‘computational biologist’. Since those early days, I have held a wide range of diverse data and analytics roles supporting various digital transformations including in R&D, Finance, HR, medical affairs and product strategy. Through these engagements, I have been able to garner a wealth of experience across bioinformatics, clinical informatics, text mining, knowledge engineering, data analysis, information architecture, AI engineering, enterprise data architecture, large scale data and analytics product deliveries and more recently data and AI governance.


Today, as the Head of Enterprise Data Office, I lead a team of more than 200 data professionals committed to improving how Astrazeneca manages data to enable business outcome whilst promoting responsible, compliant utilisation of data and AI. As a team, we support company in defining enterprise data and AI standards, designing, implementing and assuring procedural and technical controls for these standards, leading in data design (architecture) across the company and managing a suite of enabling data and AI governance technology services including master data management, cataloguing, search technology and data quality tooling.  


We operate in a federated model working in close partnership with functional Data Offices around the world who take accountability for functional standards and assurance. These groups support local business teams in understanding best practices in data, specific to the various business processes across geographic regions and the value chain of AstraZeneca. This allows data specialists in R&D, Supply Chain, Finance, and across AZ, who have local knowledge, to support teams on how best to operate with their data whilst providing back valuable insight into the global services and standards required to operate effectively.


Our mission is clear: to expedite data-driven business decisions by cultivating a firm and sustainable foundation for data usage, and promoting responsible, compliant utilisation of data and AI. This ongoing commitment reflects our determination to excel in this fast-paced industry.

How are you developing the data literacy of your organisation, including the skills of your data teams and of your business stakeholders?  


Setting enterprise data and AI standards, as well as provisioning data and AI tooling that is compliant by design will only take you so far: awareness, change management, and training are critical elements of our mission. Typically, we have focused on the data and AI specialists creating or partnering on role-based and platform-based training and certification programmes. The main challenge has been the rapid recruitment over the last 5-10 years of data scientists, engineers, and analysts (as the company embraced digital transformations), all with their favourite data and AI technologies, methodologies, and variable understanding of working with data in a global organisation. This has required us to develop clear preferred architectural approaches and guard rails for data and AI activities and reinforcing with tailored training programmes.


However, it is increasingly clear that we are moving to a position where many roles in the company are evolving into knowledge workers, processing and handling data requiring a different form of training, with particular focus on mitigating the risks associated with data mishandling. To support this, we are implementing a comprehensive Enterprise Data and AI Risk Management Framework to safeguard our data practices. By enhancing data literacy skills, we are creating a data-aware culture that integrates data awareness, education, and adoption into our daily operations.


A significant part of our mission is bringing data skills to every corner of the organisation. We are keen on making sure that everyone understands how we work and operate with data. This involves ensuring people have the tools and understanding needed to engage effectively with data, regardless of their role. Through education and training, we are promoting a more holistic understanding and use of data across the organisation.

What are the key challenges to your data function that you are facing as its leader? 


There are 3 key challenges we face within the company with respect to data and AI


Speed of Change:  Like all industries we are undergoing an AI revolution.  Data and AI approaches we are instantiating today will be redundant in 2-3 years.  However, the speed of change of large scale, heavily regulated industries such as Pharmaceuticals is typically in 10 year plus cycles.  How do we embrace AI in our operational processes and in our technology platforms, at speed whilst not compromising quality is a major challenge internally.


Silos and Innovation: How to embrace innovation in data and AI to accelerate the business goals without adding to the existing data and technology silos. As a company we have been successful, in part, by federating decision making and giving autonomy to functions. This means we have a lot of local standards, methodologies and technologies when comes to data and AI.   To achieve our 2030 ambitions, we need to enable cross company data reuse, transparency and interoperability requiring us to make significant changes to standardise both business processes, technology, and data.


Changing External Environment:   Rapidly maturing data and AI regulations across the globe creating challenges on how we manage, process, and consume data and execute AI. This requires us to review and reset our standards, processes, controls, and assurance measures of how we handle data across the company. Teams across Data Offices, Privacy, Quality, IT, Cyber, Legal etc, are collaborating to simplify how we operate across functions, how we react in a timely manner and create clarity for everyone in the company as to their accountabilities for data and AI.

Ian Dix
Ian Dix
has been included in:
  • 100 Brands 2024 (EMEA)