As businesses race to harness the potential of artificial intelligence (AI), one role has quietly become indispensable: the data professional.

Kaplan experts, Chiraag Swaly, Matt Rawlins, and Michael Lafferty, had a catch up to discuss what it takes to build and retain this crucial capability.

Kaplan experts

Insights were provided by Kaplan’s very own Chiraag Swaly, Matt Rawlins, and Michael Lafferty.

Matt Rawlins studied for his ACCA qualification at Kaplan from the age of 18. He joined us in December 2006, following his passion for education. Now, he serves as Director of Growth after working in education and apprenticeships for 19 years. His role is centred on supporting businesses to develop the digital and data skills they need to thrive.

Bringing over 20 years of combined experience in digital and IT apprenticeships, Michael Lafferty and Chiraag Swaly have collaborated extensively in their roles at Kaplan. Their deep background in technology and curriculum development includes partnering with numerous major training providers to develop technology-focused apprenticeship programmes.

The data challenge: big investments, limited insight

The session began by addressing a frustration shared by many organisations: despite significant investment in technology, many still rely on gut feelings when making critical decisions.

Michael highlighted how this often plays out in practice. Teams across the business still spend countless hours manually pulling figures into spreadsheets, each creating their own version of the truth. This not only leads to inconsistency but also consumes valuable time on low-impact, repetitive work.

Data silos add another layer of complexity. Sales data rarely connects seamlessly with marketing or operations data, making it difficult to achieve a single, unified view of customers or performance. And even when reports are produced, the information is often already out of date, leading to missed opportunities.

These insights set the stage for the rest of the discussion: how the modern data professional is helping organisations overcome these challenges and drive smarter, faster decision-making.

From data technician to business strategist

Building on the challenges of disconnected systems and manual reporting, the discussion moved to how the role of the data professional has evolved.

Over the past few years, organisations have shifted their expectations. Data analysts are no longer background technicians focused on cleaning data and producing reports. Traditionally, their work was reactive.

However, data analysts are now becoming strategic partners embedded within teams. They use AI-enabled tools and predictive analytics to uncover not just ‘what’ happened, but ‘why’ it happened, and ‘what’s likely to happen next.’

In short, data analysts have become translators - connecting complex data to real-world strategy and outcomes.

Field story: automation and impact at Arriva

The trio turned to a real-world example of transformation in action.

Michael talks about his experience working with Bas Ward, who is a Business Insights Analyst at Arriva and a recent graduate of our Level 4 Data Analysis apprenticeship.

He shares how at Arriva, manual Microsoft Excel reporting had become a significant challenge. Teams were creating their own reports and spending hours each week on repetitive tasks, often working with inconsistent data.

As a Business Insights Analyst, Bas led the development of a fully automated Power BI environment. This replaced manual spreadsheets with live dashboards, connecting data sources and providing a single, reliable view of performance.

But what did this mean for the business? Michael explained how the change saved significant time and improved decision-making confidence. With reporting now automated, teams could focus on analysis and action rather than data preparation.

Looking ahead, Michael also highlighted the potential of Agentic AI, which are systems capable of taking proactive actions on their own. These tools could soon go beyond identifying insights to automatically triggering next steps, freeing data professionals to focus on strategy and innovation.

The AI-native skillset

Building on these examples, Chiraag explored the practical skills that define today’s AI-native data professionals and how they need to adapt to new ways of working.

These include:

• Prompt engineering: For data exploration, it’s essential to know how to ask the ‘right questions’ of large datasets, as this democratises data analysis.
• Understanding of large language models (LLMs): Not to build them, but to understand how they work, their limitations, and how to apply them ethically and effectively.
• AI co-pilots and code generation: Using AI tools to write, debug, and optimise code more efficiently - shifting the focus from memorising syntax to defining the problem clearly.
• Advanced data storytelling: Using AI-powered visualisation to create not just charts but interactive, narrative-driven dashboards that guide leaders to actionable insights.

Building your talent pipeline

Reflecting on the importance of professionals blending core data principles with new AI-native skills, Matt asked Michael what practical pathways business leaders can follow to build this crucial talent within their organisations.

Michael outlined three main routes: hiring externally, upskilling existing staff internally, or developing talent through apprenticeship programmes.

However, while hiring experienced, AI-savvy data professionals can be an attractive option, it’s also one of the most competitive and costly routes in today’s market.

Michael shared the three main routes that employers can take:

They discussed how apprenticeships present a more sustainable solution for many organisations. This is largely because they learn within the context of the business - applying their skills directly to the organisation’s data, systems, and challenges from day one. This ensures they grow into roles fully aligned with your goals and ways of working.

Apprenticeships also build the blended data skill set described earlier in the session: strong data fundamentals combined with new AI-native capabilities needed to stay ahead of change. The result is a future-ready talent pipeline that delivers measurable ROI.

Arriva’s success story is a clear example of what’s possible when organisations invest in developing their own data talent.

Key takeaways

To help you apply these insights to your own team, we’ve put together a comprehensive handout you can download. It summarises the key takeaways from the session, including the data challenges identified by our experts, and a checklist of AI-native skills for your workforce.

Take the next steps

If you’re looking to transform your workforce, browse our apprenticeship programmes or contact us at Kaplan to learn more about how we can help.