Laying the foundational human factors to enable data, digital and AI collaboration

A global top 10 biopharma organisation recognised the need to lay foundational human factors to unlock the potential of data, digital and AI innovation, and co-operate effectively with their key internal and external collaborators in these domains. 

Figure 1. Our view of the foundational human factors to enable Data, Digital & AI collaboration

We have supported this organisation with laying these foundations over the last two years, including defining modular learning pathways to cater for all levels of data, digital and AI capability across R&D and designing career pathways specifically suited to data science-related professions.  

A key aspect of this delivery has been focussed on improving how ‘technical’ (eg data scientist) and ‘scientific’ (eg clinicians) stakeholders effectively communicate and collaborate with one another, both internally and externally. Overcoming this challenge is critical to support their bold ambition to embed and scale data, digital and AI innovation into all of their R&D programmes in the coming years.  

Learn each others’ language, be curious about others’ perspectives, build powerful partnerships  

We therefore developed an immersive and experiential learning curriculum for both technical and scientific audiences across R&D.  

For ‘technical’ (eg data scientists) audiences, key topics included: 

• Knowing the data scientist’s audiences to build an understanding of stakeholder preferences 
• Gathering effective requirements to get a clear view of stakeholder needs
• Managing stakeholder expectations to effectively navigate changing requirements and emerging challenges
• Telling convincing stories to clearly communicate the outcome and value of data science.

For ‘scientific’ (eg clinician) audiences, key topics included: 

• The core fundamentals of data science and where it adds value in their context 
• Identifying data science opportunities and shaping requirements in their scientific domain 
• The stages of a data science project and where scientific teams play an active role 
• Interpreting, challenging and communicating data science output.

Figure 2. Overview of the key learning components 

Over 400 R&D employees have completed this learning curriculum through a range of online and offline, interactive and self-study learning methods. Training champions across both technical and scientific communities were pivotal in supporting learning cohorts to translate learning into day to day action. Crucially, we have broken down the cultural barriers and stimulated true partnership between two diverse stakeholder groups who are fundamental to embedding and scaling data, digital and AI impact in R&D.  

Find out more about our work supporting powerful pharma partnerships, our latest article on how powerful partnerships can unlock digital health innovation, or get in touch with our Josh Elliott to discuss how we can help you.