Correlation does not imply causation. It turns out, however, that with some simple ingenious tricks one can unveil causal relationships within standard observational data, without having to resort to expensive random control trials. I introduce the basic concepts of causal inference demonstrating in an accessible manner using visualisations. My main message for data analysts is that by adding causal inference to your statistical toolbox you are likely to conduct better experiments and ultimately get more from your data. E.g, by introducing Simpson’s Paradox, a situation where the outcome of all entries is in conflict with that of its cohorts, I shine a light on the importance of using graphs to model the data which enables identification and managing confounding factors. This talk is targeted towards anyone making data driven decisions. The main takeaway is the importance of the story behind the data is as important as the data itself. My ultimate objective is to whet your appetite to explore more on the topic, as I believe that it will enable you to go beyond correlation calculations and extract more insights from your data, as well as avoid common misinterpretation pitfalls like Simpson’s Paradox.