From pie charts to spark lines, the world of data visualization is full of possibilities. But creating a dashboard takes careful planning and strategy – and sometimes even the most aesthetically pleasing charts, graphs, and reports fail to clearly deliver the information you audience needs. To help keep your dashboard on the right track, we’ve compiled a few of the most common data visualization mistakes even the best dashboarders are guilty of committing. By avoiding these pitfalls, you can help your dashboard (and its users) accomplish their goals through data.
1. Too Many KPIs in One Dashboard
Key Performance Indicators are the key (pun intended) to a successful dashboard experience. But using too many KPIs in one place can throw users off track and even point them in the wrong direction. Your dashboard should tell a story with data. It should aim users at their objectives. It should measure their progress. Most importantly, it should help them realize their KPIs and provide the information they need to achieve their goals.
By tossing too many KPIs into one view, you run the risk of diluting their importance in the eyes of your audience. Try to focus instead on the overall goal of your dashboard, and be judicious when picking the KPIs to visualize. If the KPI in question doesn’t support that overall goal, save it for a different dashboard.
2. Poor Data Quality
Nothing sabotages a dashboard like low-quality data. You may be thinking, how does data become low-quality in the first place? After all, data is black and white; it’s either accurate or it isn’t, right? In reality, there are many factors can influence the quality of your data and, by extension, the quality of your dashboard. To keep your data clean, create and implement a data control plan. Make sure you have someone who is in charge of data governance, and can account for sources and viability. An ETL tool can make much of the tedious data cleaning automated, so consider a tool to give this person a leg up.
3. Using the Wrong Chart Types
Choosing the right charts for your data is one of the most important decisions you’ll make when designing a dashboard. Some charts only work with certain types of information, while others are often overused. If you’re making decisions based solely on aesthetics, you might end up making it harder for people to interpret your data, not easier. This guide is a great place to start, but there are a few easy tricks to remember. A pie chart, for instance, is only useful for showing percentages of a whole as they relate to each other. Sparklines, on the other hand, are best employed when you want to give your audience an at-a-glance overview of a specific metric. Bar graphs and scatter plots, in comparison, provide more in-depth and exact data and the freedom to include variables such as time stamps.
4. Skewing Scale
Data visualization is all about data. Or is it all about the visualization? In fact, it’s equal parts accurate data and accurate visualization. Unfortunately, it’s easy to get swept up in the aesthetic side of things and forget to double check the visual elements of your dashboard. Three-dimensional charts, for instance, are notoriously difficult to read. While they might look impressive from a visual standpoint, they also make it hard for users to draw precise conclusions. Similarly, adjusting the Y-axis away from a zero baseline of your chart to highlight a specific data point can detract from its accuracy, even if you don’t change the actual information in the graph.
5. Gilding the Lily
We all know that flashy things grab the eye, but is your design working for or against your message? Simply put, any visual element you include in your dashboard should enhance your data – not detract from it. If you feel like you’re including too many visually-impressive elements and not enough information, take a step back and ask yourself, “Does the element outshine the information I want to convey?” If so, dial back the aesthetics and reconsider your data narrative. Then, adjust your visuals to strengthen – not engulf – the information users need.
6. Not Providing Context / Legends
Without context, data doesn’t mean much at all. When you assemble your dashboard, think about metrics that relate to each other, and how those metrics relate to your audience. If you find yourself compiling a dashboard with metrics that don’t relate to a common goal, reconsider its structure. Data visualization can help users draw hidden correlations between different metrics, so make sure each chart is aimed at providing context for other graphs on the same page. Additionally, provide extra information (such as a legend) if your graphs need one.
7. Color Calamities
Like every piece of your dashboard, your color scheme should serve a purpose. When it comes to color, consider two things: branding and communication. First, pick a clean and simple color scheme that represents your organization’s brand. Generally, stick to two or three colors that look good together – and don’t be afraid to leave plenty of white space (negative space) between your charts.
Then, consider what colors communicate with your audience. Use bright colors sparingly and only if you want to draw attention to a specific metric; use neutral colors to provide a visual foundation. In the end, users should only notice the colors when you want them to. Otherwise, your color scheme simply exists to enhance your data narrative.
8. Nothing Compares to You(r Pie Charts)
As delicious as pie charts are, they’re not always the best treat for your audience. Forcing your audience to draw comparisons between two side-by-side pie charts is impossible – don’t make them try! Pie charts and other compositional chart types won’t help people make comparisons. Instead, use a chart suited to comparative data like a column chart, so your audience can see correlations without playing “spot the difference” between two distinct elements.
9. Using Charts When You Could Use a Number
Sometimes, numbers speak for themselves. If a simple number can illustrate your point as well as a graph, use the number instead. By mixing charts and graphs with numbers, your dashboard will provide the visual interest users need to stay engaged – but without unnecessary visual interpretation. This HR dashboard example shows that in many cases, a number paired with a drill down can be the perfect balance between quick top level information and deeper details.
10. No Clear Purpose
Data visualization should provide clarity. Clarity for your data, your organization’s goals, and how close (or far!) they are from obtaining those goals. Additionally, you can use data visualization to help users understand what they need to do to accomplish those goals. Without this clarity, your dashboard will leave users wondering why they need it – or worse, forgetting about it completely. In a way, this pitfall is the culmination of every other data visualization snafu. On the flipside, avoiding the mistakes mentioned above will help your dashboard provide a cohesive view of your data’s purpose.
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