Visual data analytics give a whole new meaning to the phrase “painting by numbers”. Much like when painting by numbers, it can be difficult to see the final product when you begin – everything just looks like numbers and lines! However, as you begin to fill in the blanks, an image begins to take shape, and when you’re done, you have a beautiful work of art that speaks volumes. Or at least, that’s the goal.
In our data driven society, the notion that “more data is better” may not necessarily be wrong, but it can create the problem of having too much data to handle. The purpose of data is to help decision makers make informed decisions in real time, but that just isn’t happening. Instead, data analysts spend hours, days, weeks, or even months trying to make sense of all the information at their fingertips. Then, when they think they finally have it all figured it out, new data comes in and ruins everything. Data should not be so complex, and new data certainly shouldn’t ruin the bigger picture—it should add to it.
That’s where data visualization come in. With a bit of creativity and exploration, data analysts can stop trying to make sense of fragmented views of the bigger picture and instead, finally begin to fill in the blanks to create works of art that communicate clear and precise messages that can be used to improve decision-making, predict the future and mitigate risk.
Despite the fact that numbers are definitive and art is abstract (or maybe because of it), the two complement one another. Humans are visual creatures, after all, and it’s widely known that there are more efficient ways to display data than spreadsheets. So if we think of our data dashboards as data pictures, how can we use principles of art and design theory to create better dashboards?
Keeping It Abstract
When most people think of abstract art, they think of unidentifiable objects in paintings and wacky sculptures with several interpretations. However, while abstract can be “wacky,” it can also be extremely straightforward, and therefore extremely helpful when it comes to data visualization.
Take Ivan Sutherland’s drawing, “Winking Girl,” for example. This drawing is by no means realistic, but that doesn’t mean it’s not a good representation of a girl winking. Moreover, had Sutherland been more concerned with the details, as many realistic drawings are, we may not have readily noticed that the girl was winking at all—at least, not without some sort of careful study. However, because the elements of this drawing are so abstract—it uses lines and shapes that are generally understood in our culture to represent eyes, mouth, nose, and so on—they don’t draw attention to themselves, leaving the eye free to wander to the girl’s winking eye.
Resorting to abstract details in data visualization can effectively accomplish the same goal—it can draw the eye to what’s important: the message. While you don’t want to go too abstract with your visuals, it doesn’t hurt to rely on shapes, lines, and other conventional visual representations to get your point across.When designing your #data picture, draw the eye to what’s important: the message. #context Click To Tweet
Creating Delight with Visual Context
What are we trying to accomplish with our data picture? If the goal is soley to inform, a standard dashboard or set of graphs and charts might suffice. However, if we want to entice, encourage, and delight our audience, we find more utility in our abstract art influence.
A particularly poignant example of this can be found at Scientific American, which shares a graphic by Moritz Stefaner titled, “PLANT-POLLINATOR INTERACTIONS OVER 120 YEARS: LOSS OF SPECIES, CO-OCCURRENCE, AND FUNCTION.” The visual, for all intents and purposes, looks like a beehive, which sets the stage for what the audience can expect to learn. The point of the study was to track which bees were visiting which flowers in which years. Each circle, which make up the hive, represents a bee species, while lines and shapes within the circles represent how bee and plant interactions have changed. The graphic is simple yet elegant, and it paints a clear data picture that effectively conveys what would otherwise be mass amounts of data.
Sankia Barve’s infographic depicting the startup scene in India is another brilliant example of an impactful data picture. The project, HeyDay, uses visual elements reminiscent of an urban skyline to bring context to questions like “What is the most popular vertical amongst Indian startups?” Alone, these insights might be overwhelming and lacking context. Conveyed in this manner, however, the data becomes immediately meaningful to the viewer, as the visual effectively gives context to the data and, like Stefanar’s beehive image, suggests a subject.
When it comes to painting your own data picture, consider applying the minimalist principles of abstract art. Something as simple as color choice can have an enormous impact on the user experience of your dashboard, and can go beyond being legible and attractive. For example, if one were to be charting the sales of different wine varietals, simply choosing colors that evoke different blends and wine types can aid not only in the ease of data understanding, but also creates a more present link between the numbers on the screen and the real-life product they refer to.
Read next: Data in Context
Remember, visuals don’t have to just be made up of shapes, lines, and colors. You can use images of employees, products, and even your facilities to create the correct context for your data picture. The goal is the same, though: make a more impactful data visualization that display the significance of your data story to your audience.
Designing your Data Picture
Data visualization, in its purest form, is informative art, which is any visual display that communicates information. Like art, data visualization seeks to use neuroaesthetics — a science that seeks to answer questions about how art and beauty affect us — to understand why the human brain reacts to certain images in certain ways, and how we can elicit positive reactions via the use of color, form, and other design elements. So, with a healthy blend of science and art behind us, we ask the question: What design elements should you focus on when painting your data picture?
Color, for one, is key to helping the brain make sense of mass amounts of information, as color distinguishes one data set from another and helps to break down data into manageable bits. Form, which includes size, shape, orientation, curvature, etc., should also be given attention, as form aids in our pre-attentive processing, which is basically the way our brains take in the basic visual attributes of an object and identifies them. This ties in with the section above, Keeping it Abstract, and how you can use form to create accessible images. Finally, though the nine principles of design weren’t intended for data viz, there is no denying the fact that design concepts such as movement, balance, unity, and proportion all go a long way toward creating a positive visual experience. For a deeper understanding of these design fundamentals, check my last post, Data Visualization and the 9 Fundamental Design Principles.
It’s true that dashboards are designed to reveal insights and make correlations you never would have thought to make yourself, but you have to put in a bit of work to make them really work for you. If you want to create more impactful data visuals, lend a bit of creativity and exploration to the mix and you could just paint a masterpiece that speaks volumes.
For more dashboard design inspiration, click here to visit our gallery of live dashboard examples!
- 1.Quick Tip: Matching Colors = Effective Branding
- 2.Color Your Data
- 3.Data Visualization and the 9 Fundamental Design Principles
- 4.Neuroaesthetics and Informative Art
- 5.Painting by Numbers: Designing Your Data Picture
- 6.Developing Your (Color) Palette
- 7.Get Inspired: 19 Inspiring Data Viz Designs
- 8.INFOGRAPHIC: Fundamental Principles for Data Visualization Design
Get the Guide Fundamental Design Principles for Dashboards
Even if you’re not the artistic type, this guide will have you thinking like a graphic designer and making informed choices that support your data narrative.