Article
Disaster! Dirty Data, Data Misinterpretation, 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 realising that decisions were made incorrectly because of it. Especially when it has 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 have realised that I would 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 was not interested in changing jobs. But at the same time, I asked myself: what could I significantly contribute there? It is just too simple a business: they buy and sell, buy and sell. It did not 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 is when the work becomes enjoyable for me. That is when half a day flies by like half an hour.
This “jungle” can look very different depending on the situation. I do not want to go into specifics; I will 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. Many of these disasters begin with one confusion: mistaking correlation vs causation, reading a cause into data that only shows two things moving together.
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Summary
Common questions on this article's topic
What are the main types of data quality problems in organisations?
Why is data misinterpretation more dangerous than missing data?
What does it mean to act on data without context?
How does flow state relate to solving complex data problems?
Why do some people thrive in messy data environments?
How can organisations reduce the risk of data-driven disasters?
What is garbage in, garbage out (GIGO)?
What is dirty data?
What is data literacy and why does it matter?
What is the difference between data quality and data integrity?
What is data governance?
What is data interpretation and why does it go wrong?
Why do data driven decisions still go wrong?
If you have any thoughts, questions, or feedback, feel free to drop me a message at mail@richardgolian.com.
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