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A dashboard earns its keep when people can read it quickly, trust what they see, and know what to do next. Poor chart choices, cluttered layouts, missing context, and visual tricks turn that simple task into detective work. Dashboardland is full of such cautionary tales, along with practical ways to set them right.
Key takeaways:
Once upon a time, a perpetually busy boss was sitting in his cozy chair, gazing at the myriads of figures unwinkingly. “I requested a brief overview of the current situation in the company, so why am I lost in the labyrinth of tables and dependencies?” he thought, guessing that the night would be sleepless…
This is a fairy tale of the present. When business analysts swear that “a newly prepared intuitive dashboard will give an instant understanding of the financial health”, the chances to spend an unforgettable night wandering through the jungle of data are high. So, brew strong coffee and take your glasses if you’ve opened the dashboard like this:
But no worries, we’ll do our best to develop this story into a tale with a happy ending. No magic or forbidden tricks, let’s simply expose some common data visualization mistakes made by BI developers and business analysts. This knowledge will arm you to the teeth and you’ll never fall for the bait of meaningless metrics or overloaded tables.
Before we start bringing bad data visualization examples to light, our BI Superhero is riding to the rescue to equip you with a short checklist that’ll help to identify easy-to-read dashboard samples at a glance.
A well-designed dashboard should communicate its message almost instantly. If users need more than a few seconds to figure out what they’re looking at in an attempt to decipher the interface, the layout probably needs simplification.
Good dashboards feel almost effortless to read. Their purpose is obvious, the most important information stands out naturally, and every design choice supports understanding. Clear typography, well-chosen charts, restrained use of color, and thoughtful spacing help users focus on the data.
Whenever an element doesn’t add clarity, be it a decorative 3D effect, excessive labels, or unnecessary visual noise, it’s usually better left out.
What happens if these rules are ignored? Let’s walk through basic functional and perception mistakes that might occur.
There is a great temptation to exclaim, “Everything!” Where are the axes? How to compare positive and negative values? This is a totally unsuccessful data visualization format that raises endless questions in your head. What about the option below? Looks much more vivid, right?
In a perfect world, you’re looking through the dashboard and get all high-level info you need in a split second. Extra details are quickly shown on demand, thanks to the drill-down capacities.
Instead, you’re getting buried by the tons of data and metrics all crowded in one dashboard. Forget about this nightmare and look how to consume massive amounts of data step by step, not fearing to drown in them.
“Less is more” should be a golden rule to recollect and use before duplicating units of measures in each row or adding two decimal places even if they don’t carry a meaning.
Ask yourself a question, “Do these two decimal points make the data more precise?” or “What’s the reason to constantly repeat one and the same word instead of leaving it once in the annotation?”
If this question is rolling off your tongue all the time you’re looking at the dashboard, that’s a bad sign. Dashboard is a dynamic tool that leads to action and data-driven decisions, and it shouldn’t make users confused.
The trickiest reports sometimes try to disguise themselves as dashboards, but it’s easy as ABC to expose them. Interactive elements, like tooltips, filters, drilldowns, and links to detailed reports, mark “live” dashboards from static reporting pictures.
Burn after reading… as they’ll tell you nothing. Or can you come to any business conclusions after getting this? At least, a plan-fact sheet or comparing different time periods, e.g., today vs. yesterday, this week vs. this week a year ago, etc., is required.
A dashboard’s job doesn’t end with displaying information, it should also help users act on it. But even the best dashboard can frustrate users if it overlooks the small details. Make it easy to export or save reports, clearly indicate when the data was last updated, and provide an obvious way to contact the right person if something goes wrong.
Congrats, we’ve dealt with functional mistakes. No time to relax, though, as even trickier perception mistakes are coming.
How would you feel if the speed indicators were partially out of your sight? Scrolling an instrument panel hardly seems a user-friendly solution.
Dashboard composition is of no less importance and hugely dependent on the questions it answers. So careful consideration of all elements placement from the very start is much wiser than adding a scrollbar as a “killer feature” that’ll turn out to be a trouble in the end.
Are you staring at a dashboard for a couple of minutes already, failing to get the gist of it? It doesn’t necessarily mean you’re walking in the clouds. The reason is more likely to lie in the lack of focus on essential info, just like in the dashboard below.
Oxagile is ready to check it out and make your data analytics represented more clearly, if needed.
Good dashboards don’t give every element equal weight. Instead, they establish a clear hierarchy that draws attention to the metrics that matter most before leading users to charts, explanations, and supporting details. A well-planned grid keeps this flow consistent across the entire dashboard.
Doesn’t the upgraded dashboard look better?
It seems we’ve already mentioned the “less is more” rule today. The same thought comes into mind when dashboards are dazzling with a variety of colors, elements, captions, and fonts.
Bright and noticeable? Definitely yes.
Easily readable? We doubt that.
How about doing a quick test?
a. Do you feel that John’s earnings were much more significant in 2021 than Jack’s?
b. Is global warming not so terrifying, according to the temperature shifts during the period depicted to the left?
c. Does a 3D chart tell us that vacuum cleaners were the least popular according to the recent sales?
If you answered ‘yes’ each time, you’re likely to become the victim of visual manipulation. Truncating the axis, concealing unwanted data, using 3D distortion effects, and playing other visual tricks don’t make a high-quality dashboard.
What’s more, it’s a question of trust, so faithfulness is the best companion of reliable dashboards.
They say one picture is worth a thousand words. But what to do if the pic is so garish that makes eyes bleed?
No comments, just rules to prevent your dashboards from looking so much queer:
Pie charts are not always a piece of cake, especially when you’re overdoing with its use. If the temptation to decide on a pie chart is too high, at least don’t ignore a couple of basic principles:
A pie chart should add up to 100%, so don’t be lazy to count up every time. Otherwise, a ridiculous situation is unavoidable, like, say:
It’s of primary importance to stop in time, and a number of pie chart’s sections is no exception.
Having only one metric to introduce, either as a part of the whole or a KPI level? Just take a look at donuts, as they’re better in it.
Of course, we’re not leaving you without some overall extra tips from our BI Superhero who knows everything about perception specifics.
Simplicity is often a matter of prioritization. Keep the dashboard focused on the information users need most. Limit the number of KPI cards to avoid overwhelming the screen, and group related metrics when you need to display more.
For time-based charts, use a continuous date axis so trends remain easy to follow without excessive scrolling. And don’t forget to give every dashboard and chart a descriptive title that immediately tells users what they’re looking at.
How do all of the rules and tips work in practice when a subpar dashboard is waiting in a queue to be revamped? Feel the difference after taking 5 simple steps.
Step 1. Neutralize the color palette.
Step 2. Capitalize headings and add some context to titles.
Step 3. Skip the color legend, moving color meanings to the title.
Step 4. Change the visualization types: a horizontal bar instead of a pie chart for the right area, a horizontal stack bar chart for the main part.
Step 5. Improve the composition, dividing the dashboard into 2 parts and adding titles for meaningful parts.
Voilà!
Beware that data visualization mistakes have much more side-effects than restless nights and failed attempts to sort the dashboard through. Wrong or postponed decisions are not rare, especially if you’re sick and tired of exploring the dashboard and make yourself a promise to look at it later (read: never).
All those users who have to deal with incorrectly designed dashboards will hardly feel inspired and motivated to introduce data-driven suggestions (especially when this data is hard to perceive).
Although it’s impossible to generate an ideal dashboard in two clicks, experienced business intelligence software development specialists are good at making all preliminary work to reproduce the dashboard able to address a great diversity of business issues.
We design BI solutions and dashboards people actually use to spot insights.
Good data visualization in dashboards makes the main point easy to understand and keeps the data in context. Bad visualization uses cluttered layouts, unsuitable charts, weak labels, or distorted scales that make interpretation harder.
Common real-life examples include dashboards packed with too many metrics, pie charts with excessive slices, 3D charts that distort proportions, and graphs with missing labels or unclear units.
Misleading examples in dashboards include truncated axes, uneven time intervals, selective data ranges, and visual effects that exaggerate small differences. These choices can create a false impression even when the underlying data is accurate.
