Disaster! Messy Data, Misinterpretations, and Nonsensical Actions

I have a peculiar relationship with messy data. On one hand, it can drive me mad – there are times when I explode like a volcano after realizing that decisions were made incorrectly because of it. Especially when it’s been going on for a long time and has had a significant negative impact. And particularly when I realize that I could have identified the issue much earlier. On the other hand, resolving such situations pulls me into a state of flow – a state where I immerse myself deeply into the problem and shut out the outside world.

I’ve realized that I’d probably be bored in a place where everything is perfectly organized, all information is accurate, everyone knows precisely what the data tells us, and everyone can place it into the broader context of the organization.

One example of such a place is an overly simple organism. In the past, when I was approached with a job offer from one of the most renowned Slovak e-commerce projects, I wasn’t interested in changing jobs. But at the same time, I asked myself: what could I significantly contribute there? It’s just too simple a business – they buy and sell, buy and sell. It didn’t excite me at all. I saw no intellectual adventure in it, no opportunity to dive into entirely new situations and learn something new while solving them.

My place is elsewhere – in the jungle. Somewhere that at first glance seems chaotic and impossible to navigate. A place where most people only know their specific area of expertise. And that’s when the work becomes enjoyable for me. That’s when half a day flies by like half an hour.

This “jungle” can look very different depending on the situation. I don’t want to go into specifics; I’ll keep it general, though I realize that might make it less clear for the reader. It starts with it being one of those more complex organisms. And in such an organism, you might encounter four types of challenges related to working with information and the disasters that can arise from them.

The first challenge arises when a responsible person doesn’t work with the data they should be using to make decisions. This could be due to a lack of focus, knowledge, or time – or, worst of all, a deliberate disregard for the data, even though they know that understanding it would lead to better decisions.

The second challenge involves inaccurate data – for example, incorrect numbers. In many organizations, this might mean statistics, reports, or dashboards that don’t work properly due to errors in data collection or calculations.

The third challenge is having accurate data, but it tells us something different from what we think it does. For instance, a report on the number of certain items might, for logical reasons, not display some items it should, or it might only show them after applying a specific filter.

The fourth challenge is knowing what the data tells us but failing to place it into the context of a complex system. Essentially, we don’t fully understand what the data is communicating or which relationships need to be considered to work with it meaningfully. Acting based on data without understanding the broader context can lead to disaster.

The third and fourth challenges are closely tied to data interpretation. I could give you examples where an apparent “0” in some statistics didn’t actually mean “0” in reality. Instead, when someone with a deep understanding of the system looked at it in context, they knew exactly what that “0” represented and recognized the complexities of the situation.

Here’s the interesting part: you might think that in your organization, you’re looking at all the data you should, that you have accurate data, and that everyone (not just you, but all your colleagues) interprets it correctly and acts confidently on it. But beware, this is often the source of the biggest disasters. In larger organizations, I find it very likely that you’ll run into one of these challenges sooner or later. So here’s my advice: stay vigilant.

We live in an era where the accuracy and speed of information are incredibly valuable. The opposite of that – poor use of information, inaccuracies, or misinterpretations – significantly increases the likelihood of disasters. Let’s focus on reducing these risks by improving how we work with data. It makes sense.