Data visualization is an excellent way to depict massive volumes of data. Data visualization allows viewers to quickly absorb information, draw conclusions, or make choices.

Visualizations can be helpful, but only if they strike the right balance between being engaging, informative, and simple to understand.

Creating an effective data visualization requires more than simply displaying data in attractive visuals. You might encounter instances where visualization is poor, leading to more confusion than clarity.

This article will discuss some of the worst ways to visualize data on websites.

1. Striving To Be Extra-Creative

The goal of every dashboard is to provide an accurate and precise picture of the data it’s displaying.

Sometimes analysts get carried away with the design, forget about the standard criteria for data analysis, and make poor use of charts and graphs, leading to the most common and costly data visualization blunders.

Therefore, it is essential to understand the functions of the various graphs and when to apply them. For example, a pie chart is not an excellent choice to display time series, although it may appear flawless on the dashboard.

Pie charts will be the best if you have five or fewer variables. Every variable must be comparable and connected to the whole in some way. The most effective way to display time series will be to use a line chart or line graph.

2. Doing Away with Labels

The omission of labels is another terrible instance of representing graphs. Clear labels render the graph simple to read and provide no room for uncertainty on the viewer’s part.

In Power BI, for example, you can add labels to your charts by activating the data labels feature under the “format” menu item. Because it’s easy to forget, it’s a good idea to check data labels more than once.

It’s not enough to merely apply data labels; they must be legible. For example, when you use contrast colors, it will aid in reading labels embedded within images.

Think about the impact it will make on your audience. When you present your engagement rate, the percentage is legible as 9 instead of 90. One thing you should hold dearly is always to keep your audience in mind when creating visualizations.

3. Using Excessive Colors, Shapes, and Texts

The right color can establish mood and convey a message, but an overload of hues can be distracting. The same holds for texts that are excessively long or have too many shapes.

Readers will likely become lost in a map with many small topographical shapes, bright colors, and lengthy text captions.

Furthermore, it will be challenging for decision-makers to sift through everything and pick out the data that needs their immediate attention.  

Simple dashboards are better at getting the message across. Rather than compressing all data in a single chart, you might break it over numerous charts, which will collectively tell the audience your story.

Ideally, you should:

  • Only use bright colors to draw attention to important information.
  • Apply a color standard. For instance, if you display the United States in green on one graph, maintain that color across the dashboard.
  • Ensure the colors are consistent with your brand image.
  • Remember that certain hues have psychological connotations. For example, green suggests positive, such as revenues, whereas red is associated with losses or KPI misses.

4. Inaccurate Scales

Since they influence how we understand the background and context of the data, scales are of utmost importance. In addition to irregular increments, other scale mistakes include dark lettering and missing zero on the y-axis.

Inaccurate scales will produce charts that even experts might not understand, and that’s why people seeking to distort or transform data into misinformation will use them.

5. Leaving Out Data

Some professionals believe it is preferable to leave out data rather than provide false information when analyzing data. While they are correct that lying about facts is disastrous, leaving out data is just as awful.

Doing so will leave room to insert fictitious trends and risk overlooking essential insights. Furthermore, people will interpret your visualization in many ways, leading to a wide range of inferences.

Data schemers will purposefully leave out information to deceive readers. Alternatively, it might be due to laziness on the developer’s side, who wants to simplify their work by omitting data points such as dips and spikes.

6. Unwarranted 3D

Visualizations in three dimensions can be quite helpful in examining the relationship between variables; however, they are sometimes overused. Some 3-D charts make it extremely difficult to read the percentages of every bar since others obscure some bars.

Visualizations should be clear and understandable regardless of the data you want to display. Adding extra dimensions may allow you to display more data points simultaneously but at the sacrifice of comprehension, which is not ideal.

There’s more to data than attractive graphs and charts. The more streamlined the process of comparing your data is, the better.

7. Bad Data

The adage “Garbage in, garbage out” is a well-known concept in computer science. Poor data will result in poor visual representations when dealing with data visualization.

Start with the fundamentals: is your data clean? Before visualizing your data, use checks at the collection, source, cleaning, and compilation stages. Frequent mistakes include data duplication and unmarked NA values. To avoid inaccurate data, you can adopt Qlik Sense extensions.

A Qlik Sense extension will help you improve your visualization capabilities. You can add the extensions using the drag and drop technique. Make sure you have installed the Qlik Sense extensions properly.

Final Thoughts

Visualizations are typically more effective than words when conveying a complex idea. Proficiency in data visualization is valuable because of its wide-ranging practical applications.

However, many data displays are ineffective, which we must expect, given that human beings are prone to making mistakes.

But the good news is that many data visualization blunders can easily be fixed. Your upcoming dashboards will undoubtedly be more effective & error-free if you take note of the above worst ways to visualize data on websites.