Data literacy: the emerging skill commercial learning teams cannot ignore

At Learning Technologies 2025, surrounded by demonstrations of AI-driven platforms, autonomous learning ecosystems, and endless talk of the future of work, one insight stood out more sharply than anything else. The most critical skill for learning leaders was not building VR simulations or mastering prompt engineering. It was something much simpler, and far more fundamental: data literacy.

In a world where artificial intelligence accelerates everything, content, insights, expectations and, being able to reason with data is no longer optional. It is the price of admission for learning teams that want to stay credible in the boardroom.

How scenario planning and job advert data uncovered the critical skills

For our presentation, in partnership with Instinct Resourcing, we started with real-world evidence: hundreds of anonymized L&D job adverts, cleaned and analyzed using large language models to spot patterns in the chaos. Then, borrowing methodology from the strategic playbooks of Shell and McKinsey, we built out four emerging futures for learning and development:

• Performance accelerator: Learning becomes a business ROI machine, measured in productivity gains and operational outcomes.
• Employee experience hub: Learning powers culture, wellbeing and engagement, as organizations compete on experience rather than perks.
• Reskilling catalyst: Learning shifts into workforce transformation, helping companies redeploy and retrain talent at speed.
• Autonomous learning ecosystem: Learning becomes decentralized, AI-driven, and seamlessly embedded in day-to-day work.

Finally, based on the four scenarios, we asked the audience of learning professionals what skills they were prioritizing for their own development.

Across every future, and across all the audience responses, one theme recurred. Data literacy was not a bonus skill for specialists. It was a foundation for everyone.

Why data literacy matters now 

The commercial pressures on learning teams are growing. Business leaders are asking harder questions. They want learning investments that translate into operational performance, sales figures, and retention rates—not just happy sheets and course completions.

At the same time, the tools we use are producing more data than ever before. AI-driven platforms promise dashboards, insights and predictive analytics. But raw data is not insight. It is only useful in the hands of those who know how to interrogate it, understand its limits and turn it into a story that matters to decision-makers.

Without this fluency, L&D risks becoming either a service function at the mercy of IT departments, or a sidelined cost center unable to justify its existence.

What data literacy really means for learning professionals

Data literacy is not about becoming an advanced statistician. Nor is it about learning to create and deploy dashboards.

It means being able to:

• Understand the difference between correlation and causation
• Ask critical questions of the data you are given
• Spot misleading metrics and false proxies
• Link evidence clearly to business outcomes
• Make better decisions based on data
• Build persuasive cases for investment based on real-world performance shifts

It is, in essence, the commercial language of learning. Teams that master it will find themselves at the heart of strategic conversations. Those that do not will struggle to be heard.

Why data-literate learning teams win

Data-literate learning teams do not simply report on activity. They report on impact. They tie learning initiatives to operational key performance indicators (KPIs). They show how onboarding improvements reduce time-to-productivity, or how a new leadership program cuts attrition rates. Measurement is embedded within their performance initiatives rather than being added as a bolt-on afterwards.

This ability to translate effort into outcomes changes the nature of the conversation with business leaders. Learning moves from being seen as a cost to being seen as an investment.

Moreover, data fluency protects learning teams against technology disruption. AI will not replace thoughtful human analysis any time soon. Platforms may automate reports, but interpreting the messy realities of human performance still requires judgement, context and the ability to challenge.

In every future we mapped at Learning Technologies 2025, whether performance enablement, reskilling, engagement or autonomy, this skill was a non-negotiable.

How to start building data literacy in your team 

No grand transformation is needed to begin. Small, deliberate steps can make a big difference: 

• Upskill for interpretation, not just operation: Teach teams how to question data sources, not just how to generate charts.
• Embed better questions in your process: At the start of every project, ask “What business outcome are we trying to affect? What evidence will show whether we are succeeding?”
• Pilot smarter reporting: Choose one initiative this year to measure differently. Focus on its impact on business performance, not just participation rates.

Small wins build credibility. Credibility builds influence.

Choose your future

The scenarios we explored at Learning Technologies 2025 are not theoretical. They are already starting to unfold. And while organizations may blend elements of several futures, learning teams cannot try and cater for all theses futures forever.

The ones that thrive will be those that move first—not just to adopt new technology, but to think differently about evidence, performance, and value.

Data is no longer an optional skill for learning leaders. It is the language of the future. The only question is whether you are ready to speak it.