If you've ever sat in a meeting, looked at a slide full of numbers, and quietly hoped nobody would ask you to comment on it, you're not alone. When I ask people why they feel uneasy around data, I almost always hear some version of the same line: "I was never good at math."
It's such a common response that we rarely stop to question it. But here's the thing: data literacy and maths ability are not the same skill. In fact, treating them as interchangeable is one of the biggest reasons data literacy initiatives inside organisations stall before they ever gain traction.
In my experience, the people who find data hardest aren’t usually struggling with the numbers themselves. More often, it comes down to confidence. Somewhere along the way, whether at school, from a manager or through years of watching the ‘data person’ take over in meetings, they’ve picked up the idea that working with data is solely a maths problem, and one they’re not equipped to solve. It’s a belief I come across all the time, and I don’t think it holds up.
The real reason people say, "I'm not a numbers person"
I often see leaders equate data literacy with numeracy, and when they do they unintentionally shut capable people out of the conversation. These are people who can reason clearly, challenge assumptions, explain complex ideas simply and make sound decisions but who have quietly decided they're "not data people" because of a maths class from twenty years ago.
That belief is costly. It means valuable perspectives go unspoken in meetings. It means people defer entirely to analysts instead of contributing their own context and judgement. And it means data conversations stay shallow, because half the room have already opted out before the discussion even starts.
The maths is already done for you
Here's what often gets missed: most day-to-day data work doesn't actually require advanced statistics or manual calculation. Modern systems, dashboards and reporting tools handle the heavy lifting long before a number ever reaches a screen. The aggregation is done. The calculations are done. The visualisation is done.
By the time a number appears on a dashboard, the calculations have already been done. What’s left for the person reading it is to make sense of it: to work out what it’s showing, what might sit behind it, and whether it’s enough to act on. That’s interpretation, and it draws on quite different strengths from the ones most of us were tested on in maths class.
What data literacy actually looks like
If data literacy isn't about crunching numbers, what is it? It's the ability to look at data and ask sharper questions, such as:
What is this actually showing me?
What might be influencing this pattern?
What's missing or not being measured?
What other information do I need before I trust this?
What are the limitations of this data?
None of these questions needs a calculator. They rely on curiosity, which most people already bring to other parts of their work without a second thought. Yet because data has historically been taught as a technical, right or wrong discipline, many people approach it with anxiety instead,, chasing the "correct answer" rather than building a useful perspective, in much the same way maths was taught (and judged) at school.
How this shows up in the workplace
I see this play out in much the same way across the organisations I work with:
People stay quiet in meetings the moment numbers appear on a slide.
Non-analysts defer entirely to the "data person" in the room, even when they have valuable context to add.
Teams avoid engaging with data unless they feel completely certain about what they're seeing.
None of this reflects a lack of intelligence. It reflects an internalised belief that data literacy is about correctness, speed, and calculation. When really, it's about clarity, judgement and asking the right questions at the right time.
Why the most data-literate people slow down
The most data-literate people I’ve worked with are rarely the fastest or the most technically precise in the room. What sets them apart is their ability to explain what data means in plain language, connect it to real decisions, and sit comfortably with uncertainty instead of freezing at the first sign of ambiguity.
This mirrors the idea of "System 2" thinking from Daniel Kahneman's Thinking, Fast and Slow: slower, deliberate, effortful reasoning rather than quick, automatic judgement. Applied to data, this looks like people who:
Ask good questions before and after the data appears
Look for the story behind the numbers, not just the numbers themselves
Recognise when data is being over-interpreted or stretched beyond what it can support
Balance quantitative data with qualitative insight and professional judgement
Data literacy is learnable, and it doesn't require advanced math.
What I find most encouraging is that these are deeply human, entirely learnable skills. Building them doesn't require becoming "good at math" or suddenly loving numbers. It requires comfort with uncertainty, the ability to put insight into plain language, and the confidence to think out loud in front of others even when you don't have every answer.
This is where I see data storytelling training makes the biggest difference. Rather than teaching people to calculate, it teaches them to interpret, question and communicate, turning raw numbers into a narrative that drives better decisions.
Rethinking data literacy as a leadership priority
If your organisation's data literacy training programs feel like they've stalled, I’d encourage you to ask a different question: are you developing the skills your people actually need, or the skills you assume they need?
When leaders stop framing data literacy as a maths problem and start treating it as a thinking and communication problem, engagement changes. I’ve watched this shift happen in teams. Once people realise they’re being asked to think rather than calculate, they start contributing. They ask questions, they bring context the analyst didn’t have, and the decisions that follow are usually better for it. Nobody needed to become a mathematician for that to happen.
I’d argue this is also where strong data visualisation matters just as much as the analysis itself. A well-designed chart or dashboard should invite people into the conversation, not intimidate them out of it. Good data visualisation makes information accessible; good storytelling makes it meaningful.
FAQ
What is data literacy, in simple terms?
Data literacy is the ability to read, question, interpret, and communicate insights from data in a way that supports good decision-making. It includes recognising limitations in data, asking clarifying questions, and connecting numbers to real-world context.
Do I need to be good at maths to be data literate?
No. Most of the maths in modern workplaces is done by the systems and dashboards long before the numbers reach you. What matters more is being able to work out what the data is telling you what it isn’t and what you might do about it. In my experience, those are skills people use every day, often without realising it.
Is data literacy training only useful for analysts or technical teams?
No. In fact, it's often most valuable for non-technical staff and leaders, since these are the people making decisions based on data they didn't personally produce. Building their confidence to question, interpret, and discuss data is often more impactful than upskilling analysts alone.
How can businesses improve data literacy across teams?
Start with thinking skills rather than technical ones. Teaching people to ask better questions, consider context and explain what they’ve found in plain language tends to build confidence much faster than statistical training alone. It’s also where data storytelling comes in, because it gives people a way to share what the data means, not just what it says.
Do you offer data literacy and data storytelling training for businesses?
Yes. I’m Dr Selena Fisk, and I work with organisations, leaders and teams to build confidence with data through practical data literacy and data storytelling workshops and programs. The focus is on interpretation, communication and making good decisions, rather than technical or statistical skills. You can find more about how we can work together at selenafisk.com or get in touch to talk about what your team needs.
Ready to build a team that's actually confident with data?
If your organisation's data conversations keep stalling because people feel like they're "not maths people," the issue may have less to do with their ability, and more to do with how data has been framed for them.
I'm Dr Selena Fisk, and I work with organisations, leaders and teams to shift the way they think about data, moving away from anxiety and correctness and towards curiosity, confidence and clear communication. Through my practical data literacy training and data storytelling training programs, your people learn how to question data, interpret it in context, and turn it into decisions and action without needing to be "good at math" to do it.
If you're ready to help your team re-engage with data (and each other), get in touch to discuss a training program tailored to your organisation.
