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.
Back to Basics
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.
2. Visualizing Goals
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.”
3. Showing a Spectrum
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.
Spanning the Decades
4. Stacks on Stacks
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.
5. Historical Context
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.
Read next: 5 Provocative Map Data Visualizations
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.
Orienting Your End Users With Maps
7. Past, Present, and Future
This map chart of the United States shows eclipse information from the past 100 years and the future 100 years. It was created in 2017, so the focus is on showing the path of the total solar eclipse happening that year. That line is shown in a bright orange color, with a bold label, while the past and future are in muted tones. The colors are also thematically linked to the subject of the data visualization, mimicking shadows falling across the country or the distinctive orange of an eclipsing sun.
By showing these lines on a map and labeling some of the major cities, you can look for trends and patterns. For example, you can see that the eclipse in August 2017 was the third time an area near Boise, Idaho was eclipsed in the past century.
Get Inspired: You can control where the attention goes when creating your own data visualizations. Use a bright color and contrast it with lighter, less attention-grabbing colors, to show your viewers what the most important part of your chart is. Also, don’t be afraid to let your data story inform color choices.
8. Data In Real Time, For Real
This animated wind map shows all current wind speeds and directions in the United States in real time. Speed is represented by lines moving slowly or quickly and the direction is shown by the way the lines are moving. It’s a great example of intuitive design, because any viewer can immediately understand what the general trends are without having to look at any of the information provided on the right side.
The legend and labeled cities serve as helpful guides, and viewers can click on the map to observe their hometown or any other area of interest in greater detail.
Get Inspired: If you can, you should have your data visualization update in real time so your viewers are provided with the most accurate, up-to-date information available.
9. Two is Better than One
These map charts from Design & Geography took a look back at the 2012 presidential election. Both charts examine the relationship between average household income had and which candidate won that area. While the top map looks very familiar and shows the country by geographic area, the second is a projection that resizes the land area based on population. The two graphs tell very different, but related stories.
The colors chosen for this visualization reflect the colors of the Republican and Democrat political parties, which is important for making the graphs easy to understand. A legend in the bottom corner is there to explain the varying shades of the reds and blues (a feature typically found on heat maps). Labeling some of the bigger cities on the map also adds clarity and provides just enough context without having to label everything.
Get Inspired: Sometimes it DOES take two to make a thing go right. If there is another variable you would like to take into account in your data visualization, consider creating a new chart/graph altogether to avoid over-cluttering one graph.
The Versatility of Circles
10. Making Comparisons
While most of the data visualizations included in this post use quantitative data, RJ Andrews uses qualitative data to visualize the routines of sixteen famous creatives on Info We Trust. He represented each day as a clock-like 24-hour cycle using a donut chart around a picture of the individual. The different colors indicate various activities, such as: work, sleep, and exercise. This design allows people to easily compare their own daily routines to those of Beethoven and Mozart.
Andrews also included anecdotal information around the edge of the donut chart to separate the sections and provide a more in-depth look into what a creative was specifically doing at that time. This is particularly useful since the activity categories are so broad.
Get Inspired: You can visualize all sorts of data. While donut charts usually display percentages, you can think outside of the box and figure out a new purpose. This example shows that you can create informative visuals without a lot of metrics and numbers, as long as you use a little creativity.
11. Visualizing 100 Brands
Visual Capitalist created this circle packing diagram with tightly-organized circles to display the World’s 100 most valuable brands in 2018. The brand circles vary in size and color according to their valuation and industry, respectively. Each circle features a clean, white-scale version of the company logo, which makes this chart a snap to read.
By organizing the data in this way, you can see who the biggest players and industries are at-a-glance. You can also dig deeper into the data to analyze the total brand value for individual companies or whole industries. You can find the valuation for the companies who don’t have it listed in their circle below in a regular table (another example of going the extra mile to make up for a shortcoming of the selected chart type).
Get Inspired: Don’t be afraid to experiment with a couple of different ways to visualize your data. Think of all the chart types that could possibly work and try them out for a test run before committing to one.
12. Making It Big (Dots)
The Pudding examined how long it takes bands to “make it” – defined as headlining a show of over 3,000 capacity. They collected data from 7,000 bands that headlines a small venue (less than 700 capacity) in the NYC area in 2013 and found that only 21 of them ended up “making it.”
This dot plot shows the different paths each of the 21 bands took to “make it,” including shows they opened (shown in green) and headlined (shown in pink). The venue capacity is depicted by the size of the dot. It’s also interactive on the site, so you can see the exact date, venue, and size when you hover over each dot.
Get Inspired: Dot plots are a good way to show a timeline and you can show a lot of information with just a single dot if you play around with the size and coloring. Also, it took even Sam Smith more than 3 years to make it. Don’t give up on that garage band yet.
Pictures & Illustrations as Units
13. Putting the Bechdel Test in Visual Context
In order for a movie to pass the Bechdel Test, it needs to have at least two women in it who talk to each other about something besides a man. One theory for why only 40% of movies pass the test, is that filmmakers unintentionally make movies about themselves, and most producers, writers, and directors are men. The Pudding put this theory to the test by creating data visualizations to examine the screenwriters’ gender for 200 popular films.
This chart uses small square pictures from each film as units of measurement, which is an interesting aesthetic choice that allows people to see specific movies that did or didn’t pass the test. The shock factor (anyone else notice how many Disney movies didn’t pass?) and interactivity of being able to hover over each picture to see the gender breakdown of the filmmakers can leave people staring at this graph for a while. Fortunately, the chart builders don’t make you count the individual tiles and calculate percentages yourself. The clear headers and percentages (with good color choices to indicate passing and failing), make this graph not just enticing but easily understandable.
Get Inspired: It’s not enough to catch someone’s attention with data visualization, you have to keep it. Think of creative ways to display your data – whether it’s a particular color scheme, chart type, or even putting a suitable picture as your dashboard background… just don’t forget to prioritize readability!
14. Turning a Graph on Its Head
This data visualization was created to accompany an article about the biggest dinosaur in history, Amphicoelias fragillimus. They compare its size to the second largest dinosaur, which is being called the Titanosaur, and a human. The best way to fully show off the difference in sizes is by using an illustration of the dinosaurs and using a dotted line to clearly label the heights.
The decision to make the Amphicoelias fragillimus bright blue makes it the focus on the graph. It is clear the darker figures are only included for comparison. In addition, instead of using grid lines, there are vertical lines helping you keep track of the differences in size. Another interesting design element of this graph is the absence of a proper x-axis. In order to use the dinosaur images, they needed to shift the graph in a way that makes it look like an x-axis. In a traditional bar graph, the names homo sapiens, titanosaur, and amphicoelias fragillimus would fall just under the x-axis line. Direct labeling makes much more sense in this example and makes the visualization as a whole look less like a graph and more like an image.
Get Inspired: Try to think of new ways to re-imagine old graphs – like this new take on a bar chart. It’s okay to ignore the old rules of data visualization in favor of ones that make sense for what you are trying to convey (as long as everything makes sense).
15. Finding a Theme
This infographic shows the change in frequency of visits at sit down restaurants and fast food joints. The red fork represents fast food, while the blue fork denotes sit down. It’s a new take on a bar graph, with the length of the fork prongs showing what percent restaurant spending is up in Q2.
The direct labeling with the name of the restaurant and percent increase make it easy to compare data from the two forks. Conclusions and additional information can be found in the margins, to help provide context to the viewer, who may not understand the purpose of the chart right away.
Get Inspired: If you need to provide a large amount of data and information, it’s best to stick to traditional graphs and charts. However, if you are in a niche industry and only want to present a small amount of information to the public, it may be worth the extra time and effort to make a fun and unique graphic to show your data.
16. Graph the Rainbow
I bet you’ve never seen data visualization done like this! Everyday Analytics decided to create a column chart using the candy from five bags of Skittles to compare the color makeups. The columns are in numerical order by bag. From there, you can see that Bag 1 was an outlier for the number of purple Skittles found in a typical bag and Bag 3 had a lot of yellows.
Get Inspired: It can be a fun exercise to mix it up and take a break from creating data visualizations on your computer. While the graphs you create using physical mediums won’t be the ones you are taking to the C-suite (only slightly difficult to transport)… they do help you work on your fundamentals and creativity.
17. Profiling the Parks
Profiling the Parks is a multi-media visualization project created using elevation data from sixteen U.S. National Parks. Triggered from a visit to Yosemite National Park, the team at Info We Trust decided to create, among other things, hand-drawn data visualizations and a video short exploring data related to our parks. As a multidisciplinary project, there were several different tools and processes used, like:
- Exploring geologic data and discovering interesting profile cuts
- Creating 2D and 3D models
- Illustrating models
- Layering illustrations while maintaining scale to build narrative
The result is a beautiful example of data storytelling, building a narrative and provide unique insight into a topic that the team was clearly passionate about.
Get Inspired: You may want to consider presenting your data in a different way, like hand drawn graphs or a video (or even both, like this example). Also, let your passion and intuition guide your interest. Data visualization is all about communication, so tell the story you want to tell.
Putting it All Together
18. Contrasting Colors
This data visualization example from Information is Beautiful combines multiple charts to create a dashboard-like infographic comparing major music streaming services. The most prominent graph that provides the names of the music streaming services and sets up the rest of the infographic is the slope chart on the right side. It was a good choice to make the most eye-catching graph the one on the right, as that trains the viewer to read the graphic from right to left (the way the creator had most likely intended it to be viewed).
The designer was faced with a couple of challenges when selecting colors to represent each service. Since the background is black and lines intersect in slope graph, each service needed a discrete bright color. These color choices carried through to each smaller chart to the left of the slope graph.
Get Inspired: Don’t feel tied to a white background, but do make sure you take advantage of contrasting colors. This infographic shows just how important it is to commit to a color scheme. Had the colors for each service not stayed consistent throughout the graphic, the creator would not have been afforded the same creative freedom to eliminate some labels and grid lines.
19. Designing Dashboards
While we would NEVER say anything disparaging about the Google Analytics default dashboards (*cough cough*), this example from Annielytics shows that you can build an even more visually appealing dashboard in the platform. It just takes a little bit of manipulation and some design tips. Here are a few tips we found most inspiring:
- Don’t just use tables (they don’t show trends as well as a graph does)
- Hone in on just the data you need (use filters and segments)
- Use donut charts instead of pie charts
Get Inspired: These visualization tips can apply to creating dashboards using any tool, not just Google Analytics. While choosing the right colors and chart types are important, the tip that will lead you to the best dashboards is to identify what’s relevant and focus on that.
What inspires you to tell data stories? Any favorite data visualizations you think should be added to the list? Let us know in the comments, or find us on Twitter @iDashboards!
Inspired to build a stunning data viz of your own? Click here to request a demo of iDashboards.
- 1.Quick Tip: Matching Colors = Effective Branding
- 2.Color Your Data
- 3.Data Visualization and the 9 Fundamental Design Principles
- 4.Neuroaesthetics and Informative Art
- 5.Painting by Numbers: Designing Your Data Picture
- 6.Developing Your (Color) Palette
- 7.Get Inspired: 19 Inspiring Data Viz Designs
- 8.INFOGRAPHIC: Fundamental Principles for Data Visualization Design
Get the Guide Fundamental Design Principles for Dashboards
Even if you’re not the artistic type, this guide will have you thinking like a graphic designer and making informed choices that support your data narrative.