I first discovered my passion for data visualization through Tableau. Because it was such a new tool for me, I quickly spent as much time as I could trying to get better at it. I found out about community challenges like #MakeoverMonday that gave me seemingly infinite data to practice on. They also came with “deadlines” (if I wanted to receive feedback on them), which made the practice feel more real and pressing.
This was all very exciting. But after looking at more handcrafted visualizations, such as those by Giorgia Lupi and Stefanie Posavec in Dear Data, I decided to challenge myself. I wanted to create a visualization without the use of any software or programming language. My idea for the visualization was to keep it relatively simple: to show how much walking I had done on my recent vacation to Europe.
By the end of this experience, I realized the visualization itself wasn’t as important as what I learned by doing it. So first, I’ll share a couple of my thoughts and takeaways.
First, I realized I was embarrassingly timid. I didn’t like the idea of putting my pen to the paper. I genuinely think of myself as a non-artistic person, in addition to a perfectionist. This combination instilled uncertainty in me because it meant I couldn’t just magically make my drafts disappear (I suppose I could have burned them, but that’s getting a little dramatic). I had to learn to accept mistakes as part of the creative process, unlike when using Tableau and I could just use the “Clear” icon when I didn’t like the draft.
Second, I found that the process was more iterative. That is, I formed my initial idea of how I wanted the “feet” in my visualization to look, and from there created several different versions. I don’t think I would have arrived at the same version if I had first created it online, but rather would have settled for the chart most similar to what I wanted.
Third, and most importantly, I found the process to be much more meditative than going straight to the software. I attribute this to the fact that I spent a significant amount of time thinking about what I wanted to do, before doing it. I thought about the colors, the type of chart, the layout and size of the graphics, as well as how much text should accompany it. This forced me to reflect on the type of data I had, and to think about what that data represented to me (and thus what I wanted it to convey). This was the spend-more-time-with-your-data part of data humanism that I had never previously attempted.
This reflection on the data had me intuitively storyboarding, or representing how I wanted my ideas to flow, before diving into the project.
This sounds obvious, but it hasn’t always been that way for me. When I think back to the fact that I’ve learned data visualization through software, I see that I’ve missed this fundamental step. The Tableau canvas has been my pen and paper. While I explore the charts, I think about the layout and feel of my data, not before. I tell myself not to be concerned with color and style just yet, but at the same time I try out different palettes and labels.
This necessary separation of brainstorming and planning and creating is summed up concisely in Cole Nussbaumer Knaflic’s book, Storytelling with Data:
When it comes to storyboarding, the biggest piece of advice I have is this: don’t start with presentation software. It is too easy to go into slide-generating mode without thinking how the pieces fit together and end up with a massive presentation deck that says nothing effectively.
Additionally, as we start creating content via our computer, something happens that causes us to form an attachment to it. This attachment can be such that, even if we know what we’ve created isn’t exactly on the mark or should be changed or eliminated, we are sometimes resistant to doing so because of the work we’ve already put in to get to where it is.
Even though this quote refers to creating presentations, the same goes for creating visualizations. I can’t tell you the amount of times I’ve finished a visualization, only to decide that the layout is all wrong, and proceed to spend hours reorganizing the pieces.
A Review of ‘Storytelling With Data’
Cole Nussbaumer Knaflic’s book is an accessible resource for data viz practitioners, clients, and everyone in between
Through creating this visualization, I learned the importance of incorporating sketching and storyboarding into the design process, and plan to increasingly weave it into my practice.
Before I show the final product, I want to mention a couple of things that might have been hindered by Tableau, but became easier by hand. I recognize that some or all of these could have been achieved by using a combination of different applications, e.g. Illustrator plus Tableau. However, in this article I only want to compare the hand-drawn process against my go-to software.
In the hand-drawn visualization it was much easier to achieve a personal quality. I was able to add handwritten notes next to certain data points. I also used watercolor pencils, which allowed me to have softer, more blended edges. A scale of the number of ‘toes’ allowed me to represent how high the temperature was; if it is possible to accomplish this representation in Tableau, it would probably require a work-around.
Another benefit of not using software was that I was more selective about which data to include. For example, although each flower represents a week, I only numbered four dates as reference points. Thinking about it now, I probably instinctually did this because the dates themselves weren’t very important. In the resulting viz the days almost melt together, and the reader’s attention is drawn to comparing the more important information: distance walked, location, temperature etc.
I don’t mean to imply that one should never use software when creating visualizations. There were certainly aspects that took more time by hand, such as drawing out the size of each foot to fit the rose chart template I used. On the other hand, going from a pie ‘slice’ (or petal?) to a foot design would’ve been much more tricky in Tableau (not to mention that the rose chart isn’t a default chart, so it already requires a little extra work).
When deciding which tools to use — whether it be a ruler, Python, chart creation apps or a mix, it really all depends on your skills, the purpose of the visualization, time constraints, and I’m sure a whole lot of other factors. This article isn’t meant to help you decide which to use, but rather to urge you to consider going outside of your comfort zone (especially if you’re ‘non-artistic’ type like me) and perhaps at least begin the process with pen and paper.
I hope you enjoy the final visualization below and, as always, comments are very much appreciated!
For more information on this topic, I suggest listening to the Data Stories Podcast episode with Eva-Lotta Lamm.