How I Went From Data Avoider to Data Advocate (and Why It Changed Everything)
For a long time, I saw data as something other people were good at. I failed my maths GCE O Level three times – yes, I’m showing my age just by saying that – and carried that sense of shame with me well into adulthood. It quietly shaped my professional identity, steering me away from anything that looked too numerical or analytical. “Not a data person” became part of how I saw myself.
But that belief started to unravel in my early 40s, during maternity leave. I decided to enrol in adult education and retake maths. This time, I passed – and gained my GCSE. That small win changed everything. It wasn’t just the qualification; it was the realisation that I could do it. That I was capable. And that being good with numbers didn’t mean solving complex equations – it meant being curious, asking great questions, and following the story the data was trying to tell.
When I returned to work, I found myself looking at things differently. I started reading the company’s annual report, digging into financial and operational data, and connecting it back to our work in L&D. What began as quiet curiosity turned into a fundamental shift in mindset – and a growing confidence in my ability to engage with data on my own terms.
At the time, I was in a global L&D role at E.ON, one that required strategic thinking and foresight. The business was going through major digital transformation, and I kept noticing a persistent issue: we didn’t really know how people were learning to do their jobs. We had completion rates and learning hours, but little insight into the actual behaviours, motivations, or experiences that made learning stick.
So, I proposed a project: Learner Insights. With support from my manager, we launched a deep-dive initiative combining 36 structured questions with focus groups. It ran globally across all areas of E.ON, and over 4,600 people took part. It wasn’t just the scale of the data that made it valuable – it was what we did with it.
We treated the data not as abstract metrics, but as a story to be understood. We listened for the narrative beneath the numbers – the frustrations, blockers, workarounds, and signals of what was really going on in the system. It helped us move beyond surface-level diagnosis and understand the context people were operating in.
Crucially, it also gave us permission to challenge assumptions. Why were some learning offers being ignored? Where were opportunities being missed? And how could we design interventions that actually fit the way people worked and learned in the real world?
That project gave me a foundational belief I carry to this day: data is a bridge between strategy and lived experience.
Today, I work with organisations in much more advanced data environments, particularly around skills. AI tools now allow us to extract skills from job histories, project outputs, and career patterns. We can see not only what people claim to know, but what they’ve actually demonstrated through work. But here’s the catch: skills data is only useful if it’s trusted.
That’s where skills validation comes in. It’s not enough to generate long lists of inferred capabilities – we need human-in-the-loop checks, business-relevant evidence, and thoughtful governance to make skills data meaningful. Validation helps ensure we’re not just filling in profiles, but building confidence – in employees, in leaders, and in the systems making decisions from that data.
When done well, this combination of skills, intelligence and validation creates the foundation for better workforce planning, career mobility, and investment decisions. It also gives L&D the insight it needs to move from reactive to strategic.
I recently spoke on a podcast about skills-based organisations and said something that resonated:
“It’s exciting… but it’s also exposing.”
Why? Because once you start surfacing the skills your organisation has – and the gaps it needs to close – L&D is in the spotlight. If we’re not actively closing those gaps, we’re not doing our job. And that comes down to one thing: great data.
Great data to form your workforce and skills architecture. Great data to align learning with business outcomes. And great data to build credibility, capability and confidence in the people function and across the business.
So, if you’re in L&D or HR and still feel uneasy about data, I hear you. But data isn’t about being a maths whizz. It’s about asking better questions, listening at scale, and looking for patterns that help you act with purpose.
Because if someone who failed her O Level three times, went back to adult learning, passed her GCSE, and now advises global organisations on skills and data…
You absolutely can too.
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