7 Common Mistakes to Avoid in Data Visualization

What is Data Visualization?

One of the most useful tools for presenting complicated material in an accessible way is data visualization. This rapidly evolving field is focused on using visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data aren’t just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve a desired outcome.

There are many kinds of data visualizations, each of which serves a specific professional purpose. Some of the most popular techniques for conveying information are:

• Maps
• Pie charts
• Bar charts
• Box-and-whisker plots
• Heat maps
• Histograms
• Waterfall charts
• Area charts
• Scatter plots
• Infographics

Because we live in an increasingly visual culture, those who know how to present information in visually engaging stories have the power not only to help make sense of past events but to offer predictions for the future as well.

Benefits of Using Data Visualization

The human eye is drawn to patterns and colors. While it’s possible to use spreadsheets and reports to take in necessary information, our brains are better able to process that which is presented in a visual manner. In a manner of speaking, data visualization is one more form of visual art. This type of visual storytelling quickly draws us in, holds our attention, and allows us to engage with the content.

There are many benefits to creating effective and informative data visualizations. A well-executed data visualization can:

• Allow viewers to spot areas that require improvement.
• Provide a way to pinpoint variables that influence customer behavior
• Improve on product placement.
• Identify frequency patterns, such as how often a product is purchased in a given area.
• Anticipate sales volumes.
• Analyze risks and address issues before they grow into problems.
• Examine relationships between productivity and oversight.
• Help users implement a roadmap for future actions.

7 Common Mistakes to Avoid in Data Visualization

The goal of a data visualization is to present information clearly to the target audience. However, there are some hurdles to accomplishing this goal to be aware of when creating visual depictions of data. The following are seven of the most common errors made by Data Analysts who are depicting their findings in a visual format:

1. Avoid creating cluttered visualizations. It’s natural to want to include as much data as possible in a visualization, especially when the data being shared is exciting. However, cramming too much visual information into a single graphic is a common mistake Data Analysts make. Cluttered visualizations that include too many visual elements, such as multiple text boxes and graphic layers, lead to audience confusion. In situations where visuals are too busy to be effectively read and understood, a more pointed focus should be incorporated. One approach is to divide the information into several graphs, each of which tells its own story. Having a non-expert audience review the chart or graph before it’s published can be helpful for providing a more objective impression of the data being displayed.
2. Include context for the visualization. When presented on their own, most visuals tell only part of the overall story. Incomplete storytelling can lead to incorrect assumptions about the data being displayed. This is why it’s essential to add a textbox or legend, some other contextualizing element into the graph or chart.
3. Include both graph axes in the visualization: the horizontal X-axis and the vertical Y-axis. Omitting either axis can lead to misinterpretations of the data, as well as incorrect conclusions pertaining to the overall message. Both the X and Y axes should start at zero and contain data point spacing that is equal to the corresponding numbers.
4. Avoid data distortions. Data distortions take place when components of the visual that have different shapes are scaled disproportionately to the others that are depicted. Distortions not only can be distracting in visuals, but also have the potential to mislead an audience. For example, if a bigger piece of a pie chart is attributed to a smaller number, the information being offered to the audience will be distorted and may lead to false conclusions.
5. Work with good data. Even the best data visualization can be problematic if the source of the data is incorrect, or if the data isn’t being interpreted correctly. It’s important for Data Analysts to select source data from trusted sources and primary sources.
6. Choose color carefully and deliberately. A common mistake inexperienced Data Analysts tend to make when working with visualizations is to incorporate either too many or too few colors. Like all stylistic elements in a visualization, selecting color should be a purposeful choice. Color plays an important role in highlighting information, as well as helping to visually compare or contrast data points. It can also be used to illustrate a steady or sudden change. Instead of viewing color as decorative, it should be incorporated deliberately and in service of the overall story, and used to enhance clarity and connect with the audience. In addition, color contrast is another element to be cognizant of when visualizing data. While color can be a powerful persuasive element, excessive color contrast may lead viewers to conclude that the value disparities are more significant than they actually are.
7. Select the most effective and appropriate visualization method. Just like color serves a specific purpose in data visualization, so does the choice of visualization method. For those who want to compare various parts to the whole, pie charts are a good option. For example, pie charts are an effective visualization method for survey results and budget breakdowns, but are not as effective for comparisons between various datasets. That’s why it’s essential to know which variables need to be communicated when selecting which chart or graph type best suits the data at hand.

Just as it’s possible to make mistakes in the data analytics process, it is also possible for those working with data visualizations to make errors as well. In order to ensure that the data being depicted is as accurate, engaging, and accessible as possible, choosing the most effective visualization method, the appropriate colors, the best data, and some overall context are essential for the success of a visualization.

Hands-On Data Visualization Classes

Do you want to learn how to create informative and engaging data visualizations? Noble Desktop’s Tableau classes are offered in-person in Manhattan, as well as in the live online setting. Noble also offers a Power BI Bootcamp, a class for those who want to master the art of creating dynamic data visualizations that can be used in business or intelligence reports.

Noble Desktop’s data analytics classes provide hands-on training for learners who wish to improve their skills in handling big data. These courses cover topics like Python, SQL, Excel, and data science. For students who are most comfortable learning in the virtual format, other providers also offer live online data visualization courses. These online classes prepare participants to process, organize, visualize, and present data in a professional setting.

If you want to find a data visualization class in your area, check out Noble’s Data Visualization Classes Near Me tool. This tool provides an easy way to locate and browse more than 200 data visualization classes currently offered in in-person and live online formats.