This blog post is Human-Centered Content: Written by humans for humans.

So many reports. So many numbers. So many bars. So many things to make sense of. Has this ever been your mindset after looking at a report or dashboard that was recently shared with you? You are not alone. The concept of mental and physical fatigue is very real. The good news is that with some visual analytics best practices and dashboard design principles, you can be a part of the solution to this problem. 

First Thing’s First

Let’s start with framing the issue a bit further, first. Devices are needed in order to view published reports or dashboards. This can be in the form of a laptop, desktop monitor or smartphone screen. When a user opens the dashboard, they are essentially using a combination of their vision and brain power to get answers to their data questions, and to locate important data details or information.  

According to John Hopkins Medical, when someone is forced to look at visualizations close up, the lens of one’s eye naturally contracts. Over a prolonged period of time, in squinting mode, the lens doesn’t have a chance to relax, and this becomes the physical fatigue that is being felt. A lack of blinking contributes as well. Then, from a mental perspective, crunching numbers and analyzing data has a mental load that is also part of the user fatigue equation. 

Oftentimes, the problem starts there — too many metrics being tracked and displayed. There are dashboards that I have seen where upwards of 15 separate KPI values sit side-by-side. And while all might be useful, not all are likely critically important. A dashboard is most effective when three to five total business metrics are concentrated on. Remember, you don’t have to use a single dashboard for everything: 

Personally, I focus on creating an experience. I want the end user “clicking” quickly to get their immediate buy in. To do that, I can display an initial set of sheets that could be a combination of numbers, bars and lines. Then, I can have the user select a header, mark or button to have more details shown. This cascading approach is helpful not just to the end user, but to the dashboard’s performance too: 

So, where should we focus our energy as dashboard developers to limit the time spent analyzing data? I would start with the pre-attentive attributes. These are the visual characteristics that our brain can identify through cues and patterns to make sense of what is being seen without a conscious effort. The ones I focus on are length, position, color, and size, although shape is technically included as well:

Using double-encoded visual aids to draw attention to the “good” and the “bad” does wonders for a report. Ultimately, you want to use visuals to draw attention to the most important information, such as trends or outliers. However, be cautious and judicious when applying these as colors. It’s easy to get into a circus and lose sight of what story the dashboard should be telling. Also, remember to consider color blindness:

Being mindful about your dashboard’s inventory will help to ensure that users are able to get in and get out. The longer they are there searching and looking for the information they need, the more that ends up frustrating the end user and having them go somewhere else to find answers. Think of a dashboard like real estate — every pixel has importance, just like how every square foot does. For negative space, use the color white for a clear visual hierarchy.  

Context, Consistency and Consumer 

As far as early-on development goes, start with a UI checklist to focus on legible and professional. Then, let the three C’s for effective dashboard development guide you through productionalization: Context, consistency and consumer. 

Ensure that context is apparent in the form of any written instructions, definitions of uncommon terms, interaction controls or filters to use. Where is the data coming from and when was it last updated? Are all of your navigation buttons intuitive and easy to use? Is your messaging concise? Are your annotations and tooltips cleaned up and put in mostly sentence form to help users understand what the visuals represent? 

When using colors, fonts and building layouts, are you keeping things consistent? When moving from one dashboard to another, does the end user feel like they have gone from Earth to Mars, or have they simply moved from one room in a house to another? Are variable names and headers and formatting streamlined throughout? Style and branding standards for your organization should be used. 

And since the end users are driving this entire experience, we should always have the consumer in mind. No matter if your dashboard is an ad-hoc request or if it’s one that will be used for long-term monitoring, we always want to have the user in mind. What do they need to know? What can be presumed that they already know? How will the dashboard get distributed, consumed and updated? Do interviews if that helps. 

Finally, when analyzing the actual attention given to a dashboard or report, the F-Pattern and Z-Pattern are a great way to dismount for this post. 

The F-Pattern was first introduced by the Nielson Norman Group, a UX research firm who used eye tracking software to determine the most common scanning pattern viewers exhibit when taking in content. Users start at the upper left corner and go right, then repeat this as they move down. The interesting thing I note about this is the amount of importance placed on things very high up and on the left side:

Z-Pattern is similar to the behavior of the F-Pattern, but has looser value placed on the content, and focuses more on structure. Using a modular design and layout that follows along with the natural eye flow of the Z-Pattern will ensure each component gets ingested. It will make updates to the dashboard easier as well. We all want to have an experience that is user-friendly, and this approach that can help to standardize that. 

The Takeaways

Use a combination of six to eight overall visuals max per dashboard. Be sure to have enough white space that the eyes have natural pivots as they scan. Build with the end user in mind. Ask for feedback. Update regularly and keep data refreshed. Use colors effectively. Be consistent with things like font types and sizes. Be concise by concentrating on the most important metrics for each dashboard. That’s it, really. 

Overall, good visual analytics best practices will help limit time spent on understanding what information the dashboard wants to convey and instead will direct users to the most important details. The quicker the speed to insights, the quicker the speed to action. And the less fatigue for all of us.