![]() ![]() The second reason is that while most persons prefer Chart 2, only a few can tell all the reasons why they like it.I have spent more than 50 hours working into my submission, and it had only 17 charts - This is about three hours of work per chart. The first reason is that creating useful graphs requires planning, time, and a lot of work.Now, why most people keep creating and delivering visualisations like Chart 1? Most of you will agree that the Chart 2 is better and does not require any explanation. Can you tell which is the good one?Ĭhart 2: Another chart exposing the same data. Let's see two charts that expose the same data. ![]() If it requires explanation, then it is not a good chart. My rule of thumb to identifying a good chart is: Simply because they are not well designed and built. And it is often the case that the charts fail to deliver the value and insights they were supposed to. ![]() Many people have to create data visualisations at work every day, from data and BI analysts to data scientists, designers, and journalists. Over the last three years, I won a total of $19,000 in prizes from those competitions. I finished in third place in 20 and got the first prize in 2020 out of more than 300 different competitors. I'm a data engineer, and my daily activities rarely include having to plot charts or conduct any kind of analysis.īut even without having lots of experience, I managed to win data visualisation competitions promoted by Kaggle for three years in a row. Okay, let's start with the truth about my background: I’m not a designer, neither a data journalist nor a data scientist. Learn how to take your data visualisation skills to the next level. ![]()
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