When you talk about graphs and charts, all too often, the first image in a person’s head is a bunch of 90s-esque charts and graphs on a powerpoint pasted from an excel sheet of some kind. Data visualization (or visualisation as the Britts spell it) is a hallmark of business meetings.
C-Suite executives love visuals. Consumers love visuals. The human brain learns better with visualized data. It makes sense, but unfortunately, the ease of access to some visualization tools tends to lead to a watered down visual appearance.
I don’t know about you all, but I’ve definitely seen visuals that should have remained tabular data.
The evolution of technology hasn’t just had a negative effect on the field of data visualization in our business meetings. On one hand, almost everyone can comprehend and create graphs, charts, and diagrams—as attested to by the presence of Venn diagrams in viral humor throughout social media.
On the other hand, this means you’ll run into a lot of poor examples of data visualization.
As big data gets bigger, it will become steadily less comprehensible in its raw state. This means that people will rely more and more on data visualizations to make sense of the vast amounts of data they’re working with.
And this obviously means you’ll have to know how to effectively utilize data visualization techniques. According to a study by the popular digital marketing software Salesforce, the majority of businesses think they leave too much data unanalyzed and nearly half struggle with data outcomes.
Meanwhile, a separate study by the Aberdeen group found that visualized data made it 28% more likely that managers gain insights in a timely manner. An MIT study found that people can process images in as little as 13 milliseconds.
Visualizations turn abstract data into visual patterns, which the human brain can process much more easily. And once it’s in that state, all you have to do is remember these patterns—and the deviations from them—rather than entire sets of data.
The obvious answer is to respond with: comprehensibility, applicability, and appropriate use of charts and graphs. But that answer falls short. Those factors make for decent visuals that fall under the category of average or functional visual data.
To get great visualizations, you need to go beyond that. Great data visualizations are quick to decipher and easy to be remembered as well as being appropriate to the data they represent.
Feel free to use the TapClicks visualization infographic to evaluate how you should visually format your data.
Feel free to also check out the library of data visualization articles for information on things like: how to calculate data viz value, how to best use color for visual data, and our ultimate guide.
Back to business —
To make it on the list for best visualizations, consider the following factors for 2018, 2019, and beyond:
The best examples of data visualization should be designed well to catch people’s interest. Some visualizations are lucky enough to form appealing patterns when plotted out. For the rest, good design principles (e.g. color, typography, use of space) must make up the difference.
Our brains, as previously mentioned, have evolved to recognize patterns and be sensitive to things that break them. Overly familiar shapes and colors can become a pattern in themselves. By using uncommon or unconventional visual elements, you can make your visual data easier to remember.
This diagram from 1869 of Napoleon’s march toward Russia is my personal vote for best data visualization, it’s also one of the earliest. The horizontal axis represents progress from France (left) to Russia (right), while the colored bands represent the size of Napoleon’s army—the broad beige band shows his attack; the narrow black one, his retreat.
The shapes are effective, evocative, and immediately pique interest.
Part of the draw to this type of visual is the fact that its creative, yet clear. If you’re trying to make an innovative visual to represent your data, make sure you don’t sacrifice functionality at the altar of creativity. Both can exist together.
Steve Jobs is a perfect example of someone who believed that design and functionality are inextricably linked, and it turned out that his philosophy worked out pretty well for him.
Recent visualizations have moved from static to dynamic. Viewers and users can adjust variables, apply filters, or perform other operations that affect how the data is visualized or what data is surfaced. In creating your own visualizations, you should ask yourself: what questions might your users ask when inspecting your data and how can your visualization anticipate and respond to such inquiries?
Below, you’ll find visualization examples that can still be considered innovative by current standards. That doesn’t necessarily mean they’re all from the current year, however.
Great data visualizations, after all, stand the test of time—as Minard’s map visualizes Napoleon’s march—and can provide insight into visualization techniques for years after their creation.
In 2013, a few months after New York City started its bike-sharing program, Citi Bike, The New Yorker released a visual map with interactive data representing bike availability. The visualization used live data from people’s bike rides over the course of one month of the program.
Users can pull the red dot along the slider to progress through the time and days. Counters at the bottom reflect the number of daily trips and the weather at that point, including precipitation, which of course affects people’s propensity to bike.
Meanwhile, the dots on the map shrink and swell to represent bike availability. They can be hovered over to reveal a specific number.
Visualization from The New Yorker.
The Citi Bike report is one example of how visualization can make data both timely and accessible. Because it pertained to a recently implemented public program, the information they had was widely relevant. It was data that could affect thousands of people’s daily lives. The Citi Bike data wasn’t easily comprehensible to stakeholders.
By presenting it in this visual, interactive format,The New Yorker made the data widely approachable in a time of extreme relevance. This is the perfect example of a good data visualization.
The Atlantic, in cooperation with AthenaHealth, produced “Population Healthier” as an in-depth report on an alternative approach to healthcare and medicine: community health. It takes the form of an extended case study, which is written right into an amazing data visualization that changes views as you scroll through the page.
What truly stands out in “Population Healthier” is how consistent and cohesive all the content—both textual and visual—is throughout the entirety of the report. The excerpt pictured below shows a color-coded map of the ethnic makeup of the population of Lowell (the city on which the report was made).
Those same dots, however, are superimposed on 3D renders of the city’s main hospitals, creating a sense of consistency in the piece’s many key visuals.
Visualization Example from The Atlantic.
These visualizations, however, would be greatly impoverished without the report to which they belong. Indeed, the deeper insights are provided by the prose that accompanies the data. By pairing each image with the right section of text, both are enriched. The visual data gain invaluable context, while the prose becomes clearer and more appealing thanks to the visuals.
All in all, “Population Healthier” shows just how effective visual data can be when created for and presented in a clearly defined context.
At the time of writing, the average number of Google searches per day hovers at around 3.5 billion. Each of those people is asking something—and if you’ve ever wanted to know what those questions are, Answer the Public is the perfect visualization tool to do just that.
Answer the Public visualizes Google search queries. In the example below, it’s taken a simple keyword, “data visualization,” and gathered questions people often ask related to that phrase. Note how it’s conveniently sorted them based on lines of inquiry, such as “how,” “why,” “which,” or “what.”
Visualization by Answer the Public
The radial outline is visually arresting and the spokes or branches allow you to almost literally follow the train of thought of your average Google querent.
You can filter visualizations based on the different trains of inquiry or keywords you want to see (e.g. prepositions, comparisons, related subjects), which allows for in-depth analysis.
If you were to use multiple visualizations in tandem, you could dig quite deep into a searcher’s state of mind.
Answer the Public is aimed primarily at digital marketers, whose work requires that they understand the pulse of their customers—hence the name—but with its ease of use and amazing responsiveness, it should be easy to see how it could benefit all sorts of researchers, besides.
On its surface, the “selfie” is perhaps one of the simpler artifacts of our time. The image of the photographer’s own face framed—usually quite tightly, due to the limits of technology—by a glimpse of their surroundings. Yet concealed in this act of portraiture is a wealth of information on social media, culture and society.
Through the SelfieCity project, a group of researchers has made that information more accessible. The researchers randomly sampled 120,000 Instagram photos from across five cities to get up close and personal with the phenomenon of the selfie.
Their visualizations analyze trends in expression, posture, and characteristics of the photographer-subject. These are presented in a range of visualizations available on their website. For example, the image grids below arrange sample images along axes of posture (head tilt on the x-axis, head angling upward or downward on the y-axis).
On their own, SelfieCity’s interactive data doesn’t provide much insight.
What they do is take existing information (i.e. selfies) and make them easier to use with our typical analytical techniques. However, this act of simplification has made further studies possible—you’ll find quite a few such essays in links on their homepage.
And in an age where insight is based on the collision of minds and disciplines, this sort of work (and its subject matter) represent a bold step forward for visual data.
Visualization from SelfieCity.
If you’ve ever delved into SEO then you’ve run into a website audit, which is necessary if you’re trying to diagnose site and page health. When a site has thousands of pages, it gets incredibly difficult to understand the structure of your website, which makes it difficult to optimize the flow of your site for a good user experience.
Sitebulb is the perfect example of data visualization. The idea is simple, but the effect is massive, and ultimately, the goal of a visualization is to create something impactful.
As you inspect the visual, the central bubble is the homepage, marked by authority. Each line and the subsequent green dot indicates a spoke of the site, kind of like a bicycle wheel.
Visualization from SiteBulb
Have any favorite visualizations? Comment and share with us! We may even update the article to include it. If these visuals have lit a fire under your butt to get started with data visualization, then we recommend you read our article on calculating the value of data visualization for your company.
Hopefully, you’ll be convinced to invest in visualizing your data. It doesn’t need to break the bank.
Featured Image Credit: Poppyfield
We’ve included additional visualization examples for those that like what you see here, we’re sure you’d like to take a look at some additional examples of cutting-edge data viz.
We’ve looked into several examples of data that has been visualized. For Agencies, TapClicks specializes in doing exactly this, turn boring and confusing data from over 200+ marketing channels into a beautiful marketing dashboard. Seriously though, the headache is real getting your rows of data from source after source. Just give yourself a shortcut.