Understanding Data, Analytics and AI — Richard Golian

Most people assume data speaks for itself. It does not. Data is always shaped by the questions we ask, the tools we use, and the assumptions we never bother to examine. A number on a dashboard can look precise and still mean almost nothing — or worse, mean the opposite of what we think. I work with data every day — in marketing, in analytics, in building my own tools. The most important thing I have learned is not technical. It is epistemological: the confidence with which people use words like certainty and causality in a field built on probability and inference is genuinely surprising. An A/B test does not prove causality. A correlation does not explain why something happened. And a report full of accurate numbers can still lead to terrible decisions if nobody understands what those numbers actually represent. These articles are about the gap between measurement and understanding. About messy data, wrong conclusions and the chaos that most people avoid — but where the real work begins. If you are looking for clean answers, this is probably the wrong place. If you want to think more clearly about what data can and cannot tell you, it might be exactly the right one.