Jeff Knezovich over at On Think Tanks posted some great reflections from his recent trip to the Cartanga Data Festival, breaking down why data viz isn’t just a science but also an art. Data science alone, with its emphasis on statistics, code, and often technology, can’t develop the kind of simple yet artful visualizations that we find on feature blogs like Information is Beautiful or in reports to Ministries of Health that effectively advocate for new health facilities.
One of the highlights of his post was insight into how he approaches data visualization training and design as a discipline that requires expertise in research, technology, design, and communication. Jeff unpacks (with some great resource links!) the importance of design from a visual and graphical sense, but I would argue that data viz design requires a certain level of understanding of the human experience of interacting with information. Who is your audience? How do they interact with information? What is their level of numeric literacy? How much do they care about the information you’re trying to communicate?
My team has been exploring human centered design (HCD) methods through our work on the Innovations for Maternal, Newborn,, and Child Health Initiative*. At the core, HCD focuses on developing an empathy with the beneficiaries of a program. In visualization design, identifying an audience for your visualization and keeping them at the center of your design process is key to creating something that makes information meaningful.
Applying these principles of design need not be onerous or feel intimidating for data visualization designers (though the facilitation guides and experts in this space can go deep in more involved program design). Next time you’re crafting something visual from a data set, think about these three things:
- Who am I creating this for? As yourself this question repeatedly throughout the design process, not just at the very beginning. Understand both what they say they need from your analysis, but also their latent needs and expectations. If you’re working on a more complex project, like developing a dashboard, creating personas for your different users could be very helpful.
- Prototype (sketch!), test, and iterate. Don’t be afraid to ask for feedback from your users or at the very least your colleagues throughout the design process. And don’t be afraid to make changes!
- How will my audience use this product? How will your audience feel when they see your graph, chart, infographic, video, or dashboard? How they will interpret and use the data analysis you’ve presented? These considerations are key to ensuring your visualizations are used to promote evidence-led decision making.
Have you deliberately applied principles of human centered design in your data viz design? Share your experiences & learning in the comments!
*The Innovations Initiative is led by Concern Worldwide and funded by the Bill & Melinda Gates Foundation. JSI serves as the global research partner for the project.
In today’s round up of great TED content, they shared a new feature on art made with data. While not always the most practical of examples, the work featured shows the beauty that can emerge through different visualization techniques and serve as great inspiration.
Seeing patterns and creating beauty — data visualization has become an art form. Meet five artists who use spreadsheets, archives and digital data as their paints and canvas.
Check out their great interactive library here.
This blog post from The Atlantic summarizes the work of Vintage Visualizations who have reproduced Civil-War era visualizations from the Library of Congress. These visualizations rival some of the top quality data viz examples from today, and they were done by hand. They represent the perfect data viz balance between form and function.
While a Venn diagram may not precisely be a “data visualization” in the strictest, and most quantitative sense of the word, it is a visual tool for sharing information—specifically, relationships between categories of things.
Today, the Google Doodle features a celebration of John Venn’s 180th birthday (post-mortem, of course), and the Guardian rounds up the how-to’s and how-not-to’s of building Venn diagrams.
Any day where the Google doodle features a father of effectively visualizing information is a good Monday in our book. Any favorite examples of great Venn diagrams or Venn diagram fails? Share links in the comments below!
Some of the most popular data visualizations capitalize on seemingly unrelated current events. Take, for example, the Wall Street Journal’s “World Cup of Everything Else” dashboard. Users select from a list of indicators to see which of the World Cup countries would “win” if the competition were for country with the most rainfall, biggest urban population, or highest obesity rate. These facts aren’t new, but packaging them in a tournament bracket layout brings a fresh interest to the data.
Most of us working in global health may not think that the World Cup is a prime opportunity for sharing data. But thinking outside the box may invite new users to explore our data. For many, events like the Olympics or the World Cup may be the first exposure to different cultures and ways of life. Why not be creative and encourage competition for “longest life expectancy”?
Do you have any creative ideas for linking global health data with pop culture?
To our data viz enthusiasts: it’s been a quiet few weeks thanks to busy work schedules, vacations, and the like, but we’ll be back with new posts 2-3 times a week starting next week, starting with lessons learned from using Prezi & a high resolution image to create a videographic within a limited timeframe and budget.
In the meantime, enjoy this great piece of viz & communications humor from Chris Lysy over at Fresh Spectrum, reminding us of the importance of thinking through how we report findings & what the dissemination plan is.
Hub member Laura O’Donnell shared this site with us from Heap’s Data Blog after seeing it float around on FaceBook in the past couple of weeks. She wrote…
It makes you really think a little bit more about how data visualizations can be used to manipulate the perspective of the audience or to manipulate data visually to support an opinion.
Ravi Parikh’s blog mentions three common ways data visualizations can be misleading:
- Truncated Y Axis
- Cumulative Graphs
- Ignoring Conventions
Have you seen this? Have you done this? This space could be a great way to share and seek feedback on visualizations that you’re working onto make sure you don’t fall into any of these traps.