Designing Dashboards

Are you Making These Dashboard Design Mistakes?

By Marissa Davis
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Dashboard design may seem simple – throw some charts and graphs together and start sharing with colleagues so they can make data-driven decisions. However, data doesn’t equal information, and information doesn’t equal knowledge. It’s important to consider who your audience is, and what they want to get out of the dashboard before you even get started.

So how can you ensure a successful dashboard? Check out a few common dashboard design mistakes, and some easy steps to avoid them.

Mistake #1: Not Designing with Purpose
A lot of organizations and developers dive into dashboard development without considering the purpose of the dashboard or the people that will interact with it. A lot of dashboards just contain data. And while data alone can be useful, the context of those data points matters and provides insight that will affect the design.

Dashboards are supposed to tell stories, so the first step is to understand the story that your dashboard will tell. Consider what you want your users to discover when looking at the dashboard. From there, you need to decide whether you need to include factors such as comparing data over time or categories, relating variables or distributions, and composing multiple factors.

Mistake #2: One-Size-Fits-All Dashboards
Another issue is not knowing a dashboard’s intended audience. It’s important to remember that you are speaking to an audience – and that audience is seeking to hear something that informs them, and moves them to action – to make a decision.

Your audience can range from a data analyst to an everyday business user, and all of your users think and understand data in their own way. The best thing that you can do is to simply ask your audience, “what information do you need from me and what form do you need it in? What do you need or want to understand about this data?”

For example, are they looking for a list of numbers or are they looking at whether or not a goal is being met? If the former, then a line or column chart would work, if the latter, a gauge may be a better solution. There are also cases where your audience would appreciate both a summary graphic as well as the details behind it. You want to focus on what each user’s end goal is.

You also need to consider where you user needs to view this information. The most effective analytics are delivered in the context of a user’s workflow. Embedding analytics within the applications people use every day can lead to improved analytics adoption.

>>Learn tips for delivering analytics in context in the Definitive Guide to Embedded Analytics<<

Mistake #3: Data Overload
How many times do you have to read a word-heavy document to truly understand the message it is trying to get across? Or are you like most people, who end up skimming the document or not reading it at all, because our brains give up on trying to process all the information.

The same is true when it comes to dashboards. If the dashboard doesn’t make sense right away, we may not even use the tool to make decisions.

The answer is to create simplified dashboards that only give the user the information they need and nothing more. It’s important to boil down a large amount of data into a few key ideas that will easily fit into an understandable context for that person. Having over 1,000 pieces of information may be overwhelming for a reader and is too much for our primitive brains to process.

So don’t worry – even if you are making some of these common mistakes, there are easy ways to avoid them.


Originally published March 2, 2016; updated on August 9th, 2017

About the Author

Marissa Davis is the Corporate Communications Manager at Logi Analytics. She was previously an Account Manager at LEWIS PR, where she managed the public relation activities for a number technology companies. Marissa holds Bachelor degrees in Communication Studies and Technical and Scientific Communications from James Madison University.