Plans & PricingSignup for Free

3 classic mistakes to avoid in Data Visualization

By Telmo Silva on August 20, 2015

What’s the best way to show your data?

The way that helps you make sense of it quickly, the way that will support your analysis, the way that will show you exactly the data that you need.

We’ve all been there: presented with a large color set or sophisticated graphic options, we are tempted to think esthetics over meaning.

Here are a few bad examples where users got carried away.

  • Too Much Information

Putting too many information on a representation makes it much more complicated to read. Data visualization should make people get the idea from the glimpse of an eye and not have them scratch their heads over it.

Our advice: Keep it simple, don’t try to convey too many ideas in one chart.

  • Wild Comparisons

Stacked bar charts are an awesome way of comparing differences on a data set, however you should not sacrifice the readability of your visualization for fancy arts. In the above example, it is extremely complicated to understand the actual differences in waste management between different countries as the bars are all over the place.

Our advice here: do not sacrifice readability over a fancy representation, you won’t get your message across.

  • The Wrong Scale
badscale
source: bigbangly.co.nz

Simple bar charts are one of the most common and easiest way to represent data. However, this is only the case if you don’t mingle with its basic principles.

In this example, the bars sizes are not accurately representing the data, letting readers think that the cost of a qualified lead through local SEO is about half the cost of a lead from Print & Traditional media… however that is absolutely not the case! When you look at the data, you realize the actual number is in fact… close to 8 times higher!

Our advice here: Use the right scale or you will convey the wrong message.

And here you are! I hope those few tips will help you make more sense of your data!

Happy Dashboarding!

Table of Contents

Share this Blog

Other Blogs

The Analytics ROI Crisis: Why More Data Doesn’t Always Mean Better Decisions

Your organization may be investing more in analytics every year. However, does that lead to faster, better decisions? Despite the explosion of available data, most organizations fail to see real…

Why Your BI Strategy Fails Without a Solid Data Foundation

Most “BI problems” are data problems in disguise. When inputs are inconsistent, late, or poorly modeled, changing the chart type (or even adding an additional layer of AI) just polishes…

Modular SQL: The Secret to Consistent KPIs Across Dashboards

Dashboards don’t fall apart because of pretty charts. They fall apart because revenue means one thing in Sales, another in Finance, and a third in Marketing. When every analyst writes…
All articles
We use cookies.
We use necessary cookies to make our site work. We'd also like to use optional cookies which help us improve our the site as well as for statistical analytic and advertising purposes. We won't set these optional cookies on your device if you do not consent to them. To learn more, please view our cookie notice.

If you decline, your information won't be tracked when you visit this website. A single cookie will be used in your browser to remember, your preference not to be tracked.
Essential Cookies
Required for website functionality such as our sales chat, forms, and navigation. 
Functional & Analytics Cookies
Helps us understand where our visitors are coming from by collecting anonymous usage data.
Advertising & Tracking Cookies
Used to deliver relevant ads and measure advertising performance across platforms like Google, Facebook, and LinkedIn.
Reject AllAccept