There tends only to be one way of thinking about chart choosers: based on the data.
There is a very famous chart chooser done by Dr. Andrew Abela that has traditionally been how you choose which chart to use.
I got an unexpected crash course in KPIs when I was learning about Data Visualization. Like many UX Designers starting, I knew next to nothing about them initially: the extent of my knowledge was that KPI stood for Key Performance Indicators, and they had something to do with the business. But when I started to adopt a more scientific mindset towards design, specifically about hypothesis statements, I learned more about the importance of metrics. Part of this was understanding how using KPIs with my design recommendations would usually result in more productive meetings with the rest of my team.
As UX professionals, you’re well-equipped to improve bad visualizations.
That’s the fact that I hadn’t realized until I was doing a #MakeoverMonday project.
Bad visualizations tend to have two leading causes: bad data stories and visual clutter.
But while we may be a little unfamiliar with the former, we often spend a lot of time thinking about the latter.
We spend a lot of time thinking about the best method of organizing the elements on the page to create a great user experience: those same skills can also help when trying to declutter a visualization.
But while we may notice…
As part of the learning process of creating good visualizations, you will create bad ones.
It’s inevitable, especially when learning in a field that you may not be too familiar with.
But rather than simply abandoning bad visualizations, a much better option is to think about what went wrong and revise it: doing this allows you to learn how to improve quickly.
But it can be tricky to figure out how to do this: after all, if you were the one that created a bad visualization, how can you know where to start to fix it?
In this case, one…
I had a chance to reflect on my Data Visualization journey when preparing a UX research findings presentation. Part of the reason I had begun this journey was that my research findings presentations hadn’t been going well: whether it was silence or people simply not listening to the made points, I felt like my message was not getting across clearly.
I thought that learning Data Visualization might help present the data in a better fashion. I never expected to learn a better presentation process along the way.
By exploring visualization in different fields, I was able to come up with…
One of the most challenging parts of turning data into a visualization is figuring out what chart to use.
There are tools out there, such as the Chart Chooser, which break down the type of chart you should base on what variables you have.
But that’s only part of the equation.
According to Cole Nussbaumer Knaflic, author of Storytelling with Data, the answer to the question “What chart should I choose?” should always be whatever is easiest for my audience to read.
Here’s how to figure that out.
Whether you realize it or not, by designing the user studies, talking…
There’s a small extra step that you can take to make written presentations much more effective.
Create a persona of your stakeholders based on their data needs.
This was an idea that I encountered while taking a course on Tableau: it came up unexpectedly when talking about creating visualizations for an audience.
But it made sense the more I thought about it.
Especially if your ideas, not you, are going to be at the meeting.
Until now, I’ve talked with the assumption that you’d be giving your stakeholders a live presentation. However, that’s not always the case.
You may be…
One of the hardest skills for me to learn was how to present to stakeholders effectively. After spending days, weeks, or months doing user testing, collecting data, and analyzing it, it almost seemed impossible to condense that into a single hour with stakeholders.
Many times, I either presented too much data, didn’t highlight key points, or otherwise confused stakeholders with the terminology they were unfamiliar with. I learned how to plan out my presentations, but they never seemed to be as effective as I wanted them to be.
But visiting the world of data visualization has not only taught me…
One of the questions I’ve gotten after writing about Data Visualization has been how to start learning the subject. It’s a more complex question than I realized, and it’s partially due to the prevalence of Data Science. If you’re a UX Designer interested in learning Data Visualization, but you’re a little intimidated by Data Science and coding, here’s the better approach to take to learn it.
When I first was interested in learning more about Data Visualization, I did what anybody would do: I googled learning resources for it.
And nearly every single result that came up had to do…
When I recently had the chance to sum up my research findings with visualizations, I found some gaps in my creation process.
I had learned about each part of the process, but putting it together from start to finish was another story: I was stuck with too much-unfocused data, with little clue what to cut down for effective visualization.
So when I went to seek out more knowledge about the subject, I found a guiding creative process from an unusual source: Journalism.
Here’s how I adapted their approach for my UX.
I’ve talked about this before, but one of the…