When we started FFunction, it was 2008. This was before the Infographic Boom of 2010 and at that stage people didn’t even know what data visualization was. Back then I made this dinky little Venn diagram in an effort to explain the discipline and its uses, and it is still floating around the internet on Pinterest boards and (mostly long-dead) design blogs today.
Setting up a studio in that environment (and during the Great Recession) was a gamble, but fast-forward seven years and people now really understand what we do and see the need for data-driven design as a tool to gain insight and communicate information. These days we focus on interactive data visualizations like this and this and this for clients like UNESCO, CIFF and the Canadian Cancer Society.
Over the years, we’ve learned a lot about how to harness the power of data. We no longer need to educate clients on the benefits of data visualization. However, we still have to train designers on how to design with data.
I hire very few designers, and I train them how to deal with data-driven projects myself. I tend to see the same mistakes over and over as they grapple with the challenges of this (granted, very niche) discipline, even though the designers I invite to join the team are well-educated, tremendously talented and have a knack for the kind of work we ship at FFunction.
This is a rundown of the top 5 mistakes I see graphic designers making with infographics.
1. Sending infographic resumes
At FFunction, we receive countless infographic CVs…even for non-design positions! I got one for a communications position a while back and all I could ask was: “Why?”
Infographic resumes have really jumped the shark and become incredibly overused, but the real problem is that in reality they have very little meaning. When I see “Adobe Illustrator: 40%” represented in a little donut chart, I wonder what this is getting at. Does it mean the applicant uses Illustrator 40% of the time, or he only knows 40% of Illustrator?
If he wrote “5 years of experience,” then THAT has value. That is real data I can actually use.
2. Lack of copy editing experience
With infographics, clients provide copy that usually needs to be trimmed down. A good ratio is 70% illustrations and 30% copy. You’ll have to shorten the copy, turn it into bullets and use parts of it as titles for your images.
Here’s an example. We received this text from a client:
And turned it into this:
3. Limited understanding of information hierarchy
When editing, you should put some serious thought into information hierarchy. A lot of designers can do a wonderful layout, but nothing stands out. Regardless of aesthetics, your content should be displayed so that the most important information pops, providing different reading levels for different types of information and engagement.
4. Choosing the wrong icons
Illustration is not just about making things pretty. It’s used to communicate a message quickly and clearly. There is a simple way of testing that principle: people should know at a distance (and without reading the text) what your infographic is about. Part of this is in choosing the right visuals. When working on an infographic, you have to create a lot of icons and illustrations, and often you will have to represent abstract concepts like speed, confidentiality, security, etc. Your first impulse might be to use universal symbols such as a rabbit for speed, or a fortress for security, but that makes no sense if your infographic is for a bank. You need to deliver images that resonate with your client’s audience.
5. Not knowing dataviz best practices
Of course, the most common pitfall is this: most graphic designers don’t know the rules for representing quantitative information. Simple things like pie charts being read clockwise, starting at 12 o’clock.
Where can you learn more? When a new designer walks through our door, I make them read The Wall Street Journal Guide to Information Graphics (NB: this is not a sponsored post, we just really like the book!). It takes two hours to read, and I cannot recommend it enough. You can also read flowingdata.com and Semiology of Graphics by Jacques Bertin. All of these are excellent resources that will help you learn the building blocks of representing quantitative information. After that, every project you do is going to teach you more about data visualization–every dataset, every client and every brief are different, so treat each one as a learning opportunity.