Article published in Wired Insights.
Data visualization has been a topic for a long time now, but it is only recently that this trend has started transforming the way we grasp the increasing amount of numbers, keywords, stats and indicators bombarding us. We will not be able to make sense of this endless ocean of information, unless we pay attention to the basics of handling data and presenting them in aesthetically pleasing ways.
That’s not only because we as humans learn more visually than through any other way of assimilating information, but also because we are continuously finding better ways to represent data. We illustrate them in a manner that our (visual) right side of the brain can process and forward to the analytical (left) side of the brain.
As this data visualization trend is growing, it’s become accepted wisdom that visualizations are a nifty and handy tool. They combine a back-end engine tailored for business users who are knowledgeable about analytics with an intuitive interface that lets users customize graphics in multiple ways. That’s why dashboards are becoming the everyday infographics of choice for business users all over the world. They don’t require paying a designer and, most importantly, they morph into live decision-support tools because of their real-time interactivity.
But too often, turning data sets into graphics happens with little or no respect for aesthetics. Power Points and pie charts wouldn’t be the butt of so many jokes if they hadn’t become a sad global standard. And aesthetics are not an optional part of visualizing data. They are crucial in helping us understand what we’re looking at. That’s why it is high time to think about how we all go about handling data and presenting it.
On the one hand, everyone is now a practitioner of data and of charts, and data literacy is quickly becoming as popular as the coding movement. But we’re still sorely lagging when it comes to the fundamental understanding and actual skills for visualizing that data. We need more regular users to be able to compose pictures that are truly worth a billion words.
How Can We Get There?
As Stephen Few has pointed out, “a visualization is not memorable unless its content is memorable.” (PerceptualEdge.org) In human cognition, attention, memorability and understanding are intertwined. When done well, a visual dashboard lets the eye take in a narrative that is also catchy, aesthetically pleasing, and thought-provoking. A really good visualization fulfills all three attributes.
Besides, there are plenty of business reasons to present visuals that click and stick. Someone may want their dashboard to grab so much attention in the company that it reaches the boardroom. To be so memorable that the team constantly refers to it when talking about patterns and trends. To be so easy to understand that it leads straight to business decisions. These are three important purposes of a dashboard when considering its visual design. Business users often sacrifice some of these for other needs, thinking that they can’t have them all.
Which raises the valid question: Who can be skillful and artful enough to build convincing and captivating graphics when the boss is waiting for something to look at? It’s not necessarily the creator of the dashboard who has to do it all, but more of a group effort to spot previously unseen opportunities.
Thanks to real-time updating, interactivity and collaborative features, any viewer can complete an unfinished “masterpiece.” One user may start, and others can then decompose charts, drill through measures, zoom in or zoom out on time lines and reveal new things the original creator may not have thought of or seen. In other words, today’s visualizations represent an evolution from data discovery to discovery-within-data. You can think of it this way: users gather around an interactive visualization like a group of scientists working in a lab. Whoever starts the visualization process is akin to a principal investigator, but he or she needs the rest of the group to generate major breakthroughs or “aha” moments.
One more thing: The amount of data is not just growing, data is also diversifying. From social media analytics to data generated by smart devices, the Internet of Things (IoT) movement rapidly unfolds into a “Visualization of Things” movement, and new measures (comments, views, likes, calories, laps, heart rates, the list goes on and on) are crying out for new types of visualizations.
Here, the group aspect is also key, for several reasons. People don’t just collaborate on a dashboard after it’s published, they also have to collaborate while it is being created, with their own data. What we see happening with the Quantified Self movement will come – sooner than expected – to the business world. Ingest it all, if possible in real-time, and try to present it at a high level of abstraction.
And due to recent developments (complex timelines, wave representations, 3D analytics), we are no longer talking about individual data observers. Instead, there are multiple viewers or users who can change roles in order to obtain a 360° view of the data they analyze and identify essential nodes in highly dense webs (This TED talk presents a fascinating take on this):
Zipping around events in a “Matrix”-like fashion gives people the opportunity to navigate through multiple layers of depth and time.
So dashboards hold a lot of promise to make sense of the world around us. If, but only if we think through what data goes into them and how the visuals we are building need to grab our audience. Data aesthetics are not a luxury, but a basic necessity if we want to reveal opportunities or risks, whether it’s our personal or a company’s vital data.