chart planet
data-human interaction
I also want to be a self-respecting dataviz educator, so I will write an article about pie charts. But instead of just explaining why they are good or bad, or how to use them, I will use it as an example of generic dataviz advice, highlight why they might be poor, and whether you should still follow them.
CONTINUE >>>If you have been on the internet recently, you might have noticed that dashboards are dead because now we have AI (Artificial Intelligence). But we, people in data, still consider AI as a solution looking for a problem.
CONTINUE >>>I will cover one thing you have to consider when choosing colour to make reports more accessible. In the end, I will provide one technique how to utilize it beyond just accessibility – for better guiding of the audience.
CONTINUE >>>For those coming to Data Visualization field from Data Science or Computer Science fields, all colour related stuff might seem like magic – either you have an eye for it or you don’t. And if not, then you just don’t bother too much tweaking those, however still admire someone who manages to make a dashboard look nice. Truly a colour magician!
But at the same time, you see when it is getting bad, albeit you might not know how to fix those colours exactly.
You might have heard multiple times, that beautiful dashboards are attractive, memorable and people want to spend time with them. But more often, it is enough for your dashboard just to look professional.
CONTINUE >>>We do want to make beautiful dashboards because beauty is attractive, looks tidy, reliable and helps to create impact with our visualizations. But beauty is also just a package – the value of a dashboard is in the way it helps to read the data intuitively. Forgetting all other things except beauty might lead to poor readability and a confusing experience overall, in other words makes users more frustrated than happy.
CONTINUE >>>“Considering the audience” is one of the most important pieces of advice in storytelling and data visualisation. It is generally suggested to think about their needs, goals, and ability to understand the items displayed in the visual.
This allows our message to reach them more easily and resonate with them better, but the idea of “considering the audience” itself is quite vague.
In this article, I will turn the vague suggestion “consider the audience” into something more checklist-like, which you could go through and consider the audience “considered”.
What if your client or stakeholder asks to make the dashboard you prepared to be more eye-catching, beautiful, or just cool? In such cases, we have one job – a very subtle one – to enhance UI without sacrificing UX, dataviz or information designs, maybe even enhancing them altogether.
CONTINUE >>>You can find maybe 1000 articles with “most important”, “essential” or “golden” rules of data visualization and their set will be unique every time.
Here, I will try to extract principles for data visualization from the underlying reasons why data visualization is even a thing.
Tableau is famous for following the best of the best practices in the DataViz world, but there are still plenty of ways to make misleading charts.
CONTINUE >>>Are the default settings more universal or less universal? Do different vendors have different attitudes towards what should be the default setting?
CONTINUE >>>Managers rarely have time to thoroughly explore financial presentations, that is why you need to make as much information available as quickly as possible — an easily readable data-dense visualization serves this purpose well. In my pursuit to make denser bar charts, I found five techniques that I will share here.
CONTINUE >>>I tried finding a better way to visualize profit loss statement solving some limitations of waterfall and sankey charts.
CONTINUE >>>Radial charts are far from best practices, so if a tool has them, we can consider it to be flexible enough. May this article serve you as a helpful reference.
CONTINUE >>>Stacked Bars have one major weakness, it’s difficult to compare changes of its segments over time. I tried finding a way to improve it.
CONTINUE >>>