In a data-driven economy, career growth depends on more than job titles. Employers are prioritising professionals who can navigate data fluently, interpret patterns confidently, and communicate their insights clearly across teams, tools, and time zones.

This shift formed the focus of a recent expert panel hosted by the London School of Economics and Political Science (LSE) and FourthRev: How to Unlock New Career Opportunities in 2025 With Practical Data Analytics Skills.

This blog captures the most relevant, practical insights from the panel — featuring LSE faculty, a LSE Data Analytics Career Accelerator alumna, and a data leader — and explores how applied analytics training can act as a lever for career transformation.

You can watch the full webinar recording below: 

The career outlook in 2025: Demand, urgency and upside

Data roles have grown nine times faster than the average job market. But what’s more telling is how central they’ve become to business strategy, no matter the sector. Organisations aren’t hiring for isolated technical roles. They’re looking for professionals who can think critically, communicate clearly, and contribute to company-wide transformation through data.

Much of this demand is being accelerated by the integration of AI, which has pushed data skills from desirable to essential. As businesses adopt new tools and automate routine tasks, they need professionals who can not only work with AI but also interpret the data it relies on and produces. 

Kate McDermott, Associate Director at Omnis Partners, explained how this shift is influencing hiring across sectors during the event:

“AI is driving a huge amount of the hiring that’s going on at the moment…Candidates need to be ready to start to think about how AI will impact their roles, particularly in the analytics space. It will be an enablement — for greater efficiency, for greater impact and greater performance overall.”

— Kate McDermott, Associate Director at Omnis Partners

Across the board, data literacy is no longer a siloed skill — it’s a core business capability. As organisations restructure their teams and workflows to adapt to new AI tools and larger datasets, they need candidates who can ask the right questions, extract insight from ambiguity, and move quickly from analysis to decision.

Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE and Programme Coordinator of the LSE Data Analytics Career Accelerator, captured this shift succinctly:

“Data is like crude oil. In and of itself, it’s of limited use. We need to refine it into something valuable, and that’s what data analytics is. It’s about extracting value, converting data into insight, and enabling data-driven decisions.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

Why professionals get stuck — and how to move forward

While data roles are on the rise, many professionals — even those with deep domain expertise — find themselves unsure how to build credibility with data. Some feel overwhelmed by where to start. Others struggle to demonstrate experience in a field they haven’t formally worked in, despite working with data daily.

This was true for Anna Kramer, EMEA Finance Manager at Manychat, who shared during the panel discussions how her responsibilities were increasingly entangled with large datasets. Yet, her skill set wasn’t keeping pace:

“I worked with many reports containing a lot of data. And at some point, I had to ensure the data across different reports was still coherent. I realised that my basic knowledge of Excel wasn’t enough anymore. I saw that there were new tools available that could help ensure consistency and accuracy, and I wanted to be able to use them properly.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

It wasn’t just the tools. Anna also found herself working more frequently with developers and data professionals, but lacked the vocabulary and fluency to engage fully:

“I was involved in projects implementing IT solutions for finance and compliance, and more and more, I was working closely with developers or business analysts. They were speaking in their IT or data language, and I wanted to understand them better, so our projects could be even more effective.”

Her experience speaks to a common blocker: the distance between knowing your domain and being able to interface confidently with data systems, tools, and teams. While that distance isn’t insurmountable, it requires targeted, practical learning, with a clear focus on what employers and projects actually demand.

To close that disconnect between insight and impact, Anna chose to build a data skill set that matched the scale and complexity of her role, using the LSE Data Analytics Career Accelerator.

What employers actually want: Skills that signal readiness

For professionals stepping into data-driven roles, the gap isn’t always about raw technical ability. Often, it’s about real-world application and the ability to translate that knowledge into business impact.

Kate McDermott sees this challenge play out frequently in the hiring process, particularly with junior analysts:

“What I see missing is any evidence of work that demonstrates their understanding of business problems, and how the analytics work they do connects to that. There’s a technical base in place, but it’s usually quite theoretical. It’s not clear how that skill will translate into the day-to-day of a business setting.”

— Kate McDermott, Associate Director at Omnis Partners

She explains that the technical CVs flooding hiring pipelines often lack exactly what businesses are prioritising most: contextual thinking, critical communication, and proof of skills that translate beyond the classroom.

Later in the panel, Kate explained what employers are really looking for — not just a checklist of tools, but a track record of applied thinking and outcomes:

“My advice to candidates is: if you want to stand out, you need to be able to connect the technical work that you’re doing to value creation. It doesn’t have to be commercial value. It could be time savings, efficiency, enabling workforce decisions. But if you can’t showcase that in your CV or in an interview, you’ll become part of a very large talent pool that’s technically able — but not hire-ready.”

— Kate McDermott, Associate Director at Omnis Partners

In today’s market, proven ability matters as much as knowledge. That’s why the LSE Career Accelerator combines academic credibility with hands-on project experience — blending world-class instruction with applied, real-world data work that builds confidence and a portfolio that proves you’re “hire-ready”.

How to build data skills that stick — and cut through the noise

Once you discover the demand for data fluency, the next question becomes practical: How do you learn what matters? And how do you build skills that are truly understood, not just practised in isolation?

Dr James Abdey was clear that it starts with focusing on principles, not trends.

“You need to start with a solid conceptual foundation. That’s what enables you to upskill later. The field will evolve — it’s evolving every day. New tools will emerge. But the basics of how to approach data problems, how to structure analysis, how to think critically with data — those are timeless.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

He also pointed to the difference between learning content and developing capability, and why the format matters when you’re trying to build long-term confidence:

“There are lots of MOOCs and free resources, and they’re not without value. But they can leave people with an illusion of understanding. You watch a video, you click through a notebook, but when you’re then asked to work with a messy dataset or explain your thinking, that’s when it breaks down. You want learning that’s applied, that involves feedback, and that helps you build fluency in the language of data.”

For professionals at an inflexion point — whether trying to switch careers or level up in their current field — James recommends starting with questions about outcomes, not platforms.

“Ask yourself: What kind of problems do I want to be solving? What kind of decisions do I want to influence? Once you answer that, then the right tools, techniques, and learning experiences tend to emerge pretty clearly. But if you start by chasing platforms or job titles, it’s easy to get lost.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

What you’ll actually do in a data role — and how to prepare

Understanding what data analysts actually do on a daily basis can feel vague or even overwhelming at first. But the practical day-one tasks aren’t theoretical — they’re grounded in real problems that need timely answers.

Anna offered a clear example from her own work:

“Before, when I worked with reports, I would find the answer to the question and move on. Now, I ask — what else can I get from this data? And then again: what else? I iterate. I get much more insight out of the same report. It helps me see how my team works — and that’s helped accelerate my path inside the company.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

Anna’s experience shows how deeper engagement with data can unlock new momentum in your role. Dr James Abdey reinforced this, offering a sharp distinction between knowing tools and knowing how to apply them for business impact:

“In my view, the difference between someone who knows tools like SQL and Python — and someone who can actually use them to deliver business value — comes down to structured problem solving. It’s about understanding the business context, asking the right questions, and applying your tools intentionally.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

Whether it’s identifying outliers in a product dataset, visualising marketing KPIs, or prompting a GenAI model to explore patterns, the toolkit is real and repeatable.

Tools you’ll use in many early-stage roles include:

• SQL — for querying and joining datasets
  
• Python — for automation and analysis
  
• Power BI / Tableau — for dashboards and stakeholder reporting
  
• GenAI tools — for ideation and exploration
  
• Business storytelling — to shape your insights into action
  

The LSE Career Accelerator: Structured learning for real business impact

From SQL and Python to GenAI and business storytelling, mastering tools is just the start. What sets professionals apart is the ability to apply them in context — to solve real problems, influence decisions, and communicate insights clearly. 

The LSE Data Analytics Career Accelerator is a career-outcomes-driven programme that delivers exactly that: combining world-class instruction with practical experience to build full-stack data fluency. 

Designed in collaboration with industry experts, the six-month, part-time format blends foundational theory with hands-on application, helping learners go beyond tool proficiency to deliver business impact.

For Career Accelerator Alumna Anna Kramer, the impact of data skills was immediately clear when stepping into a new leadership role:

“I was able to get deeper insights faster, see connections between different functions, and help my team move from reactive to proactive. With a proper data toolkit, you’re no longer just describing what happened, you’re actually influencing what happens next.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

From learning to doing: Practical skills and real-world experience

One of the defining features of the LSE Career Accelerator is its emphasis on real-world application. Through a final Employer Project — designed by an actual industry company — learners get the chance to apply their skills to a real business problem.

Anna spoke about this project as the turning point in her learning journey:

“The turning point was the final module — the Employer Project. I realised I could really speak with data analysts. Now I’m in cross-functional projects with our data analytics department and I feel 100% confident. They can’t tell me something’s impossible — because I know what’s possible.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

To ease the leap from foundational concepts to real-world complexity, Dr Abdey explained how the programme carefully layers each stage of learning, so by the time learners reach the Employer Project, they’re equipped to work on it with confidence.

“We scaffolded the learning carefully. You start with exploratory analysis — how to visualise and tell stories with data. Then you move into more advanced tools like Python and predictive analytics. Finally, you apply everything through the Employer Project. It’s active learning, not passive. You don’t just ‘get a certificate’ — you get experience.”

By the end, learners are equipped to work across teams, influence decisions, and deliver outcomes through data.

A career catalyst, not just a course

If you’re considering a move into data, or want to integrate data skills into your existing role, the path doesn’t have to involve starting over. The LSE Data Analytics Career Accelerator offers a practical, recognised, and flexible route to career change and advancement, guided by employer input and expert career coaching.

Over six months, you’ll build practical fluency in SQL, Python, Power BI, GenAI tools, and data storytelling — all within a curriculum shaped by industry needs. You’ll work on a real business challenge from companies like the Bank of England, VP Analytics, StudyGroup, PureGym, and GaeaAI, producing a portfolio project that goes beyond theory to show how you solve problems, communicate insight, and create value with data. 

As Kate McDermott made clear, what stands out in candidates is their ability to translate technical skills into business value and the evidence they can make this connection. This link turns skill into opportunity, and learning into real career results.

Curious to see how the LSE Career Accelerator could support your next move? Download the brochure to explore the curriculum, structure, and outcomes.