Four Principles to Make Data Visualization Clear and Compelling
September 7, 2021
The DataFace has been designing and building data visualizations since 2017. In this time we've had the pleasure of seeing our partners get their stories into major media outlets creating buzz around the causes they advocate for.
Over the years we noticed that when people are looking to create a data story, they quickly jump to ideas about slick animations, 3D experiences and elaborate interactivity. We get it, everyone loves a big and bold experience. And so do we!
But we've found that data visualization that truly has an impact is not just bold in how it looks, but rather in all the things you don't see – the strategic choices that are made, what we've decided to leave out, the data analysis, web development and target audience research.
Many hours go into the foundation of the data story and this is where the impact lies. The big bold experience built around it is actually the easy part.
So we decided to share what we believe the underlying principles are of a clear and compelling data story. We've landed on these four:
1. Data visualization is about telling a story.
Good data visualization is more than the creation of charts and graphics. It's telling a story in a way that compels your audience to internalize the data. This can't be done with a dashboard containing ten charts in a single view. We often see reports like this that were created in business intelligence tools like Tableau.
While these kinds of charts are well-organized, they don't do enough to guide the viewer. They are let loose on the data and expected to figure out what the key information is.
If we break away from this dashboard mindset we can tell a story that resonates with people, letting them internalize and care about the information. One way we do this is by finding a hook.
The hook allows us to highlight the information that will capture the audience's attention. This way people can enter complex data in an interesting and accessible way without being overwhelmed by the information. We give them an "entry point".
Choosing the hook is a matter of sifting through the research and identifying the information the audience will care most about.
Example
For Strava we went through a huge amount of quantitative and qualitative data derived from a survey taken by 25,000 runners about why they run. Instead of simply presenting a well-organized overview of the biggest motivations behind running, we illustrated an interesting juxtaposition we saw in the data: Runners dislike most aspects of running (the first mile, the midpoint, running uphill) while claiming they "definitely enjoyed" running. There's a story there that many people can relate to. So we built the data visualization around this juxtaposition.
The data stories we tell often speak to important socio-economic issues which makes them inherently emotional. Choosing the right format and providing the right context can have a powerful effect on how a data story resonates with an audience.
The format could be a traditional data story that walks the audience through the points that speak to current times and trends. It could also be a personalized experience that allows the audience to choose their own adventure based on how they relate to the data.
We choose the format that best allows people to see themselves in the data. We know that this has the biggest impact on the causes that these data viz projects are advocating for.
Example
One of the recent projects we did with Yelp is a great example of a data story that is particularly timely. Yelp has a lot of data on local business performance, which on its own doesn't tell much of a story, but when presented in the context of the coronavirus pandemic it becomes extremely relevant, emotional and relatable for anyone living in affected areas and working in the business categories that are showcased in the data story.
2. Every choice made must come down to who your audience is.
Creating a data story involves thousands of decisions. There are so many possibilities with bespoke data visualization that it's easy to let our minds run wild. But when it comes down to making choices we always go back to who our audience is.
The audience informs every decision from where the data viz should live, to how we make it shareable, what format to use and much more. That's why for each project we take the time to get to know our target audience by considering things like how tech-savvy they are, what topics they deeply care about and what aspects of the data story they would want to share.
A lot of our target audience research we use to determine what channel and format will best reach this audience. For example, if it's a PR piece that needs to reach consumers, it's important that we create a great mobile experience and that it's optimized for Google search. A PDF report won't get as very far compared to a responsive and well-optimized web page that's easily shareable on social media.
Accessibility is also a consideration here. It's important to account for people who experience the web in a different way. That's why our design choices are influenced by what works well for people with different types of neurodivergence. For example, when choosing colors we take into consideration what works well for people who are colorblind. Additionally, we work with alt tags to make the data story accessible for people who are visually impaired and using a screen reader.
Example #1
For a recent project we did for LAAUNCH, we hoped to reach a socially conscious audience who would be willing to share and amplify the findings of LAAUNCH's survey on attitudes towards Asian Americans. The goal being to raise awareness and stop hate against this group.
For this, it was important to easily be able to share specific findings within the data. So we included social media buttons in each section of the data story. When clicked, a pre-populated social post appears with a link anchored to the shared content.
Unfortunately, Instagram does not allow this kind of pre-populated post to be generated. Since Instagram is such an important platform for activism we found a way around this limitation.
We added a download button to each section that generates a well-designed chart that can be downloaded and posted to Instagram.
This plays into a developing Instagram trend in which carousels are used to break down information into digestible visual bites. This is exactly how people started sharing these charts.
Example #2
For a project we did for Equable, the audience consisted of public workers trying to find out how well their pension plan serves them. We found that this group wasn't extremely tech-savvy which impacts the way they behave on the landing page. We anticipated that they were more likely to search for their pension plan in the search bar than use the dropdown menu and that it would not be so straightforward for them to know what to type into the search bar when looking up their plan. That's why we made sure the search was able to handle many different types of input. Whether they punched in their state, profession or the name of their plan, the search results would be broad enough to capture what they meant.
Another way we served this specific audience was by adding strategic language to accompany each pension rating. We wanted it to be extremely clear to less tech-savvy users what each score means. This way no assumptions need to be made about what it means to score a 19 out of 30 because it's accompanied by the text: serves workers moderately well.
3. Design choices should be based on how human perception works and not on what simply looks cool.
It's tempting to get swept up in all of the possibilities of data visualization. There's scrollytelling, 3-dimensional experiences, gamification...the list goes on.
These are all great tools, but we've found that there's a lot more power in making use of small and subtle visual cues that take human perception into consideration. Knowing how the human mind processes information is a great tool to make complex data simple to understand. Without this focus, your data viz will be hard for audiences to wrap their heads around no matter how cool it looks.
Want more Data Viz?
Check out our newsletter.
This can be illustrated in something as simple as choosing between a pie chart or a bar chart. The thing is, people are bad at perceiving differences in area. This makes it really hard for them to see the differences in the slivers of a pie chart if it contains more than two components. In contrast, people are really good at perceiving two-dimensional differences like lengths. That's why bar charts are great, but people are hesitant to use them since they are simple. The thing is, simplicity adequately conveys information. We believe that this holds priority over what is slick and trendy.
Don't get us wrong! We love creating engaging digital experiences with amazing landing pages and bold animations, but we strive to find the balance between what is compelling and what is clear. This means that the charts can and must be simple in the end.
At its core that is what good data visualization is about – making complex data simple for your audience to understand. Don't let this get lost along the way due to a desire for a big, bold experience.
Example
For our project with Equable, we made use of small bars to show how different aspects of pension plans are rated on a scale determined by Equable. The bars accompany each rating which gives necessary context. It's small, but it helps people to easily scan and compare the information. This visual cue is then used as a consistent motif throughout the data visualization so that it becomes scannable allowing the audience to move through it swiftly and find the information they need.
These little pieces of data visualization may be small and subtle but they are powerful in helping the audience understand what they're looking at and find out what's important within data.
4. Interactivity should be used as a tool for deeper engagement, not as the foundation of a data story.
Adding interactive elements to a data visualization can be very powerful. It can also cause your data story to fall apart if used the wrong way. Here's why:
If your audience needs to click around and engage deeply with the data viz in order to discern any insights, then you've lost them before they've begun to interact. The data visualization should not rely on the user to hover, click, scroll or interact to get meaning from the experience.
That's why the default view of each data visualization needs to be meaningful in and of itself. Interactive elements can then be used to supplement the insights that are clear at the first glance. The challenge lies in creating a default view so engaging that the user is driven towards interaction.
Once that foundational experience is established, interactive elements can allow people to dig deeper into the data. We love to use things like scrollytelling and the option to enter demographic data to tailor the experience to a specific person. When someone can see themselves in the data they are more likely to engage and share that experience.
Example
For HCCI we created a comprehensive view of health care spending in North Carolina. For this project, we were dealing with an extremely rich data set. We decided to use interactive elements to organize this large amount of data and to allow people to dive deeper into health care spending in their specific county.
In order to drive people to interact, the default dashboard view needed to give the audience meaningful insights just by landing on it – no interaction required.
We successfully created a dashboard view that quickly allows the audience to discern what counties are spending more on health care than the state average. The use of colors allows people to gleam where there are pockets of low and high spending across North Carolina without clicking, filtering or toggling anything.
This default view gives enough insights to stimulate people to interact and find out more. For example, by hovering and clicking their county they can see a breakdown of health care spending by Payer Type.
We believe that the success of our projects can partly be contributed to the adherence to these four principles. They are what help us transform complex data into clear and compelling stories and help us veer away from simply adding bells and whistles to a data set. Ultimately, this results in our partners being able to create awareness for the causes they advocate for, leading to positive change.
Founder & Creative Director
Jack Beckwith
Contact Jack if you’d like to learn more about data storytelling or ways that The DataFace can help your business.
