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Generating Value from People Data: GSK’s Angela Le Mathon

by Dani Johnson and Stacia Garr | March 20th, 2024

GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. They aim to positively impact the health of 2.5 billion people by the end of 2030. 

In her role as the VP of People Data & Analytics, Angela is responsible for generating value from their people data. She has the opportunity to shape thinking and inform strategy. Her job is to translate skills so that everyone can do what they need to with the data.

She shares more about GSK’s scientific approach, how they’re using AI to gather information, and how skills verification ties in. Don’t miss this fascinating conversation. 

Using science and technology to build confidence in the data

Traditionally, HR functions were set up to hire, develop, and reward people based on their jobs. GSK’s approach to skills is an operational shift from job-based decision-making to skills-based decision-making. 

Angela points out that GSK has always hired, developed, and rewarded people based on their skills. They’re simply further exploring something they’ve always done to gain insights into the workforce. AI has enabled them to be more skills-informed at a larger scale. 

When it comes to the world of AI inference, you need data. When it comes to people data being used for decision-making, you have to be confident in it. Their strategy is to use science and technology to build confidence in the data. 

They seek to understand the quality of the data sources and how the AI models work. They’re learning what works and what doesn’t. They’re curious to understand the impact on different systems and what it means when it all comes together. What are the implications of their decisions? They have to be able to explain how the AI models work and be transparent about it. 

How GSK gathers and integrates skills data into Workday

They use one system of record (WorkDay) where they house the standardized view of skills. Because they’re starting with Workday, it’s their foundational structure. They leverage what’s existing there to answer decisions around skills. Other providers and vendors may appear on the scene but the information in Workday is their core. 

The level of skills data needed to make a decision can be different. Everyone thinks about and translates skills differently. They’re allowing different areas to explore skills in the way they need while harmonizing the data to tell a consistent narrative.  AI is helping them structure and make sense of the data. 

They also gather passive data and require active participation from the employee to see whether or not they agree with what’s been inferred. When they think about the skills required for a job, it still requires interaction and conversation. There needs to be human participation at the right points (especially with the verification of skills). 

A conversation about the verification of skills

AI can infer the skills it thinks someone has based on the information available. Managers can also validate the view. There are also self-reported skills. All three have a role to play. You may rely more on one over another for specific decision-making purposes. 

It’s difficult to verify skills because sometimes it’s more about proficiency. It’s less about having a skill but more about demonstrating a particular level of mastery in that skill. Angela is most interested in the proficiency aspect of decision-making. 

Maybe someone’s resume says “excellent communication skills” but someone else engages in frequent public speaking or is a published author. You might argue that the latter individuals have a deeper level of proficiency. 

How do we define proficiency levels? How do job architecture and skills connect? What technology is necessary to make this sort of ecosystem function? Listen to the whole conversation for a holistic view of Angela’s work at GSK.

Resources & People Mentioned

Connect with Angela Le Mathon

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