The Dangers of Personal Experience

We all do it. We trust our gut, we rely on what we’ve seen, and we make decisions based on how things seem. It’s natural - we’re human. But there’s a big difference between perception and reality. And in any organisation where decisions affect lives, services, strategy, or outcomes, mistaking one for the other can be a real problem.

It’s not just about whether the data exists. It’s about whether the people making decisions know how to read it, challenge it, and use it properly. That’s where data literacy comes in - and it matters now more than ever.

Take a fairly recent example. Kemi Badenoch suggested in a BBC interview that some communities weren’t integrating - based entirely on what she’d personally observed. Now, her experience isn’t invalid. But when it’s used to drive policy decisions that affect millions of people, it needs more than just personal observation behind it. Relying on what we see (or think we see) might feel intuitive - even persuasive - but without broader evidence, it’s just one person’s view. And one person’s view, even someone in a senior role, can’t speak for the country. Without proper data, without a clear understanding of what the numbers show, decisions risk being based on assumptions - and that’s dangerous.

When perception drives policy, we end up with stories instead of facts. Narratives instead of nuance. And that’s how people - sometimes whole communities - get left out or misunderstood.

This isn’t just a theoretical issue. It happens all the time. From the way local governments allocate resources to the policies that shape national education, housing, and health strategies, decisions based on perception alone can have far-reaching consequences. For example, a policy maker might observe that a particular demographic appears to be underperforming in some areas and, without data to support this view, decides to implement a sweeping initiative that affects everyone in that demographic. The reality might be far more complex, with many factors at play that aren’t immediately visible to the naked eye. Relying on assumptions rather than robust data could lead to misallocation of resources or, worse, to the creation of policies that harm rather than help.

And it’s tempting to think: “Well, the data team will sort that.” But if only the analysts understand what the numbers mean, or how they’ve been collected, or where the gaps are - we’ve got a problem.

Because when data is passed around without context, or when reports land on someone’s desk who doesn’t have the skills to interpret them properly, the risk is that people fill in the blanks with… well, perception.

Data is not just the realm of the information department or the "number crunchers". It needs to be accessible in appropriate forms to everyone who needs it - from managers and department heads to the front-line staff who deal with the realities of policy execution. Without proper data literacy, decisions may be made based on incomplete information or, worse, on false assumptions. The person reading the data might not understand its limitations, understand its context, or recognise when it’s being misinterpreted.

There are countless examples of business and public sector initiatives that have been launched with grand ambitions but ended up failing because those in charge didn’t know how to spot the holes in their data.

Data literacy doesn’t mean everyone has to become a statistician. But it does mean that people should have an idea of what questions to ask. Understanding that a single graph doesn’t tell the whole story. Spotting when a data point might be skewed. Knowing when something feels true - but the data says otherwise.

The thing about perception is it’s convincing. It’s what we’ve seen, what we’ve felt, what we’ve heard from others like us. But it’s also personal. It’s narrow. It comes with all the usual baggage - bias, assumptions, blind spots.

It’s easy to think that your perception of a situation is universal, that everyone else sees it the same way. But reality often tells a different story. Data shines a light on nuances and complexities that our personal experiences simply don’t capture. A recent case of this in the public sector involved the UK's approach to social mobility. A policy was enacted based on the observation that certain areas of the country weren’t advancing economically, with ministers pointing to a perceived lack of effort from the local population. However, deeper data revealed that these communities were experiencing significant barriers - from access to education to geographical isolation - which the initial perception had completely overlooked.

Reality, on the other hand, often looks different once you dig into the data. Sometimes it confirms what you thought. Sometimes it doesn’t. But either way, it gives you a wider view. One that stretches beyond your own circle, department, postcode, or timeline.

I'm not saying people shouldn’t trust their instincts - just that instincts shouldn’t run the show. That’s what data is for: not to replace experience, but to test it.

And the people interpreting that data? They can’t all sit in one office away from the business. Yes, we need analytical teams, but we also need data-literate teams at every level - in policy, HR, communications, operations, strategy. People who are comfortable asking:

“Where did this data come from?”

“What’s missing?”

“Are we drawing conclusions too quickly?”

“What does the data say - and how sure are we?”

Data literacy is not just a "nice to have"; it’s a "must have". In organisations where decisions have a direct impact on people’s lives, the ability to ask the right questions and critically assess data is crucial. Take, for instance, a policy designed to address homelessness. It might be based on a few well-meaning observations or case studies, but without proper data analysis, the initiative could miss the mark entirely. A deeper dive into local housing data, socioeconomic factors, and trends in mental health could reveal a far more complex issue than initially perceived, leading to better, more targeted interventions.

The difference between perception and reality isn’t always obvious. But if we want to make fairer, smarter, more effective decisions - in the public sector, in business, in community work - we need people who know how to spot that gap.

It starts with a workforce that understands data. Not just how to read it, but how to question it. The more people understand data and its potential to reveal truths, the better decisions we can make - whether it’s allocating healthcare resources, designing public policies, or guiding business strategies. In fact, data literacy should be part of the core skillset in any sector, not just a specialised job for a few analysts. Everyone, from senior management to front-line staff, should be equipped with the ability to interpret and act on data.

Because when everyone - not just the analysts - can tell the difference between what feels true and what’s actually happening, we stop guessing. We stop letting perception dictate policy, and we start letting data guide us to the truth.

And when that happens? We start getting it right. For everyone.