Today, everyone is talking about data visualization. Business users and analysts want to (literally) see insights into their data, and visualizations are a method to immediately and visually communicate information. A well-designed, meaningful visualization delivers an insight at a glance into data through the careful distillation of images, color, and design. It’s intuitive, and it’s easy: it doesn’t just give you the data you need, it shows you, visually, what you are looking for. For the business user, this is fantastic – it’s fast, it’s informative, and it’s valuable.
When we think of data visualization, it’s hard not to think visually. Data visualization is art after all, be it delivered via colorful and content-rich charts and graphs, carefully crafted dashboards, and/or even quirky albeit astute infographics. Used properly, visualizations communicate data in ways that offer immediate, actionable, and aesthetically intriguing insights into data. And, today’s visualization tools are enabling even more self-service – or, self-sufficient -- discovery abilities to business users, too.
Make no mistake: visualization is a very critical tool in the modern analyst toolkit, part and parcel to both discovery and communication.
But, like anything, visualization doesn’t come without its set of cautions. Getting too carried away with the visual representation of data can dilute – or worse, distort – its meaning. What could be extremely insightful presentations of data could become little more than colorful splatter paint cleverly disguised as data visualization. There’s a very important lesson here, and leave it to the data scientist to be the one(s) to articulate it.
“[It is] hard to have a meaningful visualization without extensive curation,” data scientist Dr. Nathan Halko of SpotRight, a nifty Boulder-based start-up that uses a propriety consumer social graph to analyze social activity and connections of digital audiences, posited. “It’s a question of what is really visualization, and what is just…eye candy.”
Well said, unicorn. He’s right, too. Visualizations, while pleasing to the eye, have to be more than just eye candy. The key, then, to data visualization isn’t just providing a visual representation of data: it’s in providing the right kind of visualization for the data – and visualizing it the right way.
With that in mind, here are 3 simple questions to ask to make sure your data visualizations passes the eye candy test:
It is visually approachable? First and foremost, make sure the visualization is straightforward and easy to understand by its intended audience. Then, capitalize on the fact that people perceive more aesthetic design as easier to use by including design elements – color, shapes, etc. – to make it visually appealing. This is visual design, or the practice of removing and simplifying things until nothing stands between the message and the audience. In visualization, the best design is the one you don’t see.
Does it tell a story? At their core, a visualization packages data to tell a story. Therefore, they require a compelling narrative to transform data into knowledge. Make sure your visualization has a story to tell – a story: one. Too often people want to present all the data in a single visualization that can answer many questions – tell many stories – but effective visualizations are closer to a one-visualization-to-one-story ratio. Focus on one data visualization per story; there’s no need for a mother all visualizations.
Is it actionable (or, to use a design concept: does it have affordance)? In other words, does the visualization provide guidance through visual clues for how it should be used? Visualizations should leverage visual clues -- or establish a visual hierarchy – to direct the audience’s attention. This is the “happy or uncomfortable” test: before you even know what the numbers say, the design of the visualization should make you feel something – it should compel you to worry or to celebrate.
In any case, tools for interacting with data – be it visualization or other – shouldn’t break communication between the business and IT. We’ve got to keep thinking about closing the gap between business and IT, not widening it. Ultimately, data statisticians – data scientists, perhaps – look deep into data for insights, while business users tend to look higher – to a presentation of data – for their insights. In other words, IT shouldn’t assume that business users possess what Birst VP of Product Strategy Southard Jones calls the “visualization gene” – that inherent insight as to how to morph a set of data into a true visualization – but instead, should set them up for success with the tools that help them make the right choices to tell their story.
A well-designed, meaningful, non-eye candy data visualization that leverages colors, shapes, and design can not only display, but can influence the way we receive insights into data – which is something we all can benefit from. And that’s a tasty win-win for everyone.