Get Inspired: 19 Inspiring Data Viz Designs
Ever feel like you are stuck in a rut when it comes to designing effective and visually appealing dashboards? It happens to the best of us. Creative block is real, and sometimes you just need a little jolt of inspiration. That’s why we’ve put together some of the most inspiring data viz designs to help get those creative juices flowing again.
19 Inspiring Data Viz Designs
Feel free to skim, skip around, or read the full list start to finish. We’ll start out by showing some simple, yet effective data visualization designs that are clean, lean, and follow the best practices for effective storytelling. As the list goes on, we will venture into more aspirational designs that may be harder to mimic with your own data. For each design, however, we are breaking down what we love about it and providing some key takeaways that may just inspire you to build something worthy of tacking on to this list.
1. Simple Graph, Big Decision
This simple line graph is a powerful example of how data visualization can be used to make decisions. Ann K. Emery created this graph to help a friend decide whether to continue renting his current apartment or to purchase a condominium (which you may have already gathered from the descriptive title.)
This is a great example of making a statement by using colors effectively – something that, unfortunately, isn’t always achieved in the world of data visualization (particularly for business). Ann used a muted color and an action color so she could draw attention to the cheaper, and therefore more desirable, option for her friend. The overall design is very clean and easy to read because there are no grid lines or legend competing for attention. She instead elected to directly label the end points of each line, proving that small things can make a big difference.
Get Inspired: Sometimes clear and simple is best, especially when your goal is to be able to make a quick observation or decision by looking at a chart or graph. It’s also important to go beyond the default colors, as that can help you more effectively communicate your data story.
Data visualization is most effective when it’s directing the eye. Design choices can help your audience distinguish between important and less important data without them even having to think about it. Cole Nussbaumer Knaflic demonstrates this in Storytelling with Data, with many tactics, and frequently makes use of dotted line in relation to a solid line (the typical default). One instance where it can be helpful to use a dotted line is when you are analyzing data points in relation to a goal. In this example, a solid black line could add some unnecessary confusion to an otherwise straightforward line graph. It’s better to let the goal line fade into the background, so it is present but not distracting.
With this chart design, the goal is the first thing you see, but the formatting makes it clear that the thick, colorful, solid lines are the data points you should be focusing on. Similar to the line graph in #1, the legend and grid lines are eliminated, making the dotted line stand out even more. Instead, the labels float to the right of the data points and share their colors, linking them semantically and providing the audience with context.
Get Inspired: If you are going to play around with some of the smaller details in your graph (such as whether a line should be dotted or solid, thick or thin, black or grey, etc.), make sure there is a reason behind what you are doing. Dotted lines are a great feature when used to reference a goal but should not be used to draw attention or “just because.”
Stephanie Evergreen used a diverging stacked bar chart to show the spread of Strongly Agree to Strongly Disagree for three different survey questions. This works better than regular stacked bar charts for this example because it is easier to compare the values of the categories in the middle of the bar. Allowing the colors to fade towards the middle also helps with this comparison, as it shows that these opinions are less strong than those in the darker shade.
The title is also an important part of this graph, as it clearly states the conclusion you should arrive at after looking at the data. That way, you know what to look for and why these three questions were chosen from (what I am assuming was) a longer survey.
Get Inspired: While diverging stacked bar charts certainly don’t work in all situations, they are great for showing the spread of positive and negative values (like strongly agree to strongly disagree) from a survey.
This column chart from Visualizing Economics illustrates changes in family spending over the 20th Century. While clothing, food, and housing used to take up almost all of the family budget, you can see how much that changed over time in the graph. In fact, clothing, food, and housing only took up about half of the average family budget in 2002.
The use of stacked columns in this chart is a simple, yet effective design technique. It allows viewers to easily see the parts that comprise the whole, and how they trend over time. The blue, green, and pink colors are complementary, yet easy to distinguish from one another, and the icons/labels on the side is a visually appealing alternative to a legend. The spacing between the column bars indicates that there is not an equal amount of time between each year being shown, which is a subtle way to signal that important information to viewers (so they can draw conclusions and make comparisons appropriately).
Get Inspired: Stacked column charts are a good way to compare the elements across categories, but you need to be careful to choose colors that make it easy to distinguish one element from another.
Here’s another graph from Catherine Mulbrandon of Visualizing Economics, analyzing average income from 1913-2004. She used an area chart that shows how income has changed overtime and in relation to important events.
She incorporated economic and world events that would have affected the U.S. economy into the body of the area chart (good space that often goes underutilized). This is a good way to show context and tell a narrative alongside the data, showing that average income is affected by these events. Had the economic and political events not been included, this graph would be far less compelling.
Get Inspired: If possible, always put your data in context. There are many methods to achieve this (labeling, chart choice, color, etc.) – the key is to understand what you need to communicate and then finding the right visual elements.
6. The “Hottest” Birthdays
The Daily Viz visualized births by day from 1994 to 2014 to show their popularity. The chart is a heatmap, which visualizes data through variations in colors. The more intensely saturated the square, the higher the value. It’s also interactive, so if you hover over a specific day, it lists the average number of births, the rank, and an estimated conception date.
Heatmaps are helpful when analyzing anomalies or patterns at a glance. Here, we can see that it is much more common to have a birthday in the second half of the year (July – December) and that September is the most popular birth month. Note that while heatmaps are not the best at displaying specific numerical data, the interactive hovers take care of that problem for this example.
Get Inspired: Think about the pros and cons of each chart type suitable for your data before choosing. If there are any shortcomings of the one you want to use, try to find a way around it. In this example, they add interactive hovers to provide more specific information on a heatmap. For your dashboard, it may be as simple as adding a drilldown to provide a deeper look into the data you are projecting at-a-glance.
