Richard Golian

1995-born. Charles University alum. Head of Performance at Mixit. 10+ years in marketing and data.

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Castellano Slovenčina

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AI sales forecast: 9 traps so far

Yesterday I could not tear myself away from the computer. When I lifted my head, it was half past eight in the evening. I had been sitting alone upstairs for about three hours.
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Will AI take my job? A certified Google trainer told me in June 2024 that my profession would cease to exist. Twenty-two months later, my job title has not changed — but ninety percent of what I do during the day is different. I have delegated more of my thinking to AI agents than I thought possible. I am not afraid. This is why, and what it means for anyone asking the same question.
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Richard Golian

Hi, I am Richard. On this blog, I share thoughts, personal stories — and what I am working on. I hope this article brings you some value.

Disaster! Messy Data, Misinterpretations, and Nonsensical Actions

Data quality errors, bad decisions

By Richard Golian

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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 realising 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 realise 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 realised that I’d probably be bored in a place where everything is perfectly organised, all information is accurate, everyone knows precisely what the data tells us, and everyone can place it into the broader context of the organisation.

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 realise 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.

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Summary

Messy data, misinterpretation, nonsensical actions. Most people avoid this chaos. I thrive in it. The paradox: frustrated by bad data, deeply engaged when resolving it.
Richard Golian

If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.

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Common questions on this article's topic

What are the main types of data quality problems in organisations?
In the article, four distinct challenges are identified from practical experience. First, decision-makers not working with the data they should be using. Second, inaccurate numbers — errors in collection or calculations. Third, accurate data that tells something different from what people assume it tells. Fourth, correct data that is not placed into the broader context of a complex system. The third and fourth are the most dangerous because they create a false sense of confidence.
Why is data misinterpretation more dangerous than missing data?
Because missing data is visible — you know something is absent. Misinterpreted data feels like knowledge while leading to wrong conclusions. In the article, examples are described where an apparent zero in a report did not actually mean zero — only someone with deep understanding of the system could recognise what the number truly represented. Acting confidently on misunderstood data often produces worse outcomes than acknowledging you lack information.
What does it mean to act on data without context?
It means making decisions based on numbers without understanding the relationships, dependencies, and business logic behind them. In the article, this is identified as the source of the biggest disasters in organisations. A report may be technically accurate, but if the person reading it does not understand how the system works, they may draw conclusions that seem logical but are fundamentally wrong.
How does flow state relate to solving complex data problems?
Flow — the state of complete immersion in a task where time disappears — was described by psychologist Mihaly Csikszentmihalyi as occurring when skill level matches challenge difficulty. In the article, messy data environments are described as triggering exactly this state: the complexity is high enough to demand full attention, the feedback is immediate, and the problem is meaningful. Half a day can feel like half an hour when deeply engaged in untangling data chaos.
Why do some people thrive in messy data environments?
In the article, this is explained through preference for intellectual challenge. An overly simple system where everything is perfectly organised offers no opportunity for deep problem-solving. The jungle — a complex organism where most people only know their specific area — is where the most valuable analytical work happens. The ability to navigate chaos and connect information across domains is described as the core skill.
How can organisations reduce the risk of data-driven disasters?
In the article, the advice is to stay vigilant — even when you believe your data is accurate and your team interprets it correctly. In larger organisations, encountering one of the four data challenges is not a possibility but a likelihood. Improving how people work with data — checking assumptions, understanding context, and questioning whether the numbers mean what they appear to mean — is the most practical way to reduce risk.