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What are the most common mistakes when creating data visualizations?

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Data visualizations are powerful tools to communicate complex information, patterns, and insights. However, they can also be misleading, confusing, or ineffective if not designed properly. In this article, you will learn about some of the most common mistakes when creating data visualizations and how to avoid them.

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Eliyahu Ravuna

Sr. Software Engineering Manager at Johnson & Johnson MedTech (Biosense)

1 Choosing the wrong chart type

One of the first decisions you need to make when creating a data visualization is what type of chart to use. Different charts have different purposes and strengths, and choosing the wrong one can distort or obscure your message. For example, pie charts are good for showing proportions, but not for comparing values or trends. Bar charts are good for comparing values, but not for showing relationships or distributions. Line charts are good for showing trends, but not for showing categories or frequencies. To choose the right chart type, you need to consider your data, your audience, and your goal.

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Ryan T.

Engineering impactful and experiential solutions
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In my experience, what usually needs to be conceptualized first is the story: What is the story that you are trying to tell and who are you telling the story to? The same data can tell different stories to different audience. Once the story and audience have been agreed on, it will be much easier to decide the type of charts to use and how the charts should flow to bring out the story.

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2 Using too many colors, fonts, or effects

Another common mistake is to use too many colors, fonts, or effects in your data visualization. While these elements can enhance the aesthetics and readability of your chart, they can also distract, confuse, or mislead your viewers if used excessively or inconsistently. For example, using too many colors can make your chart look cluttered and hard to interpret. Using too many fonts can make your chart look unprofessional and inconsistent. Using too many effects, such as shadows, gradients, or 3D, can make your chart look distorted and inaccurate. To avoid this mistake, you need to use colors, fonts, and effects sparingly and strategically, and follow some basic design principles, such as contrast, alignment, and hierarchy.

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Tofig Kareemov

Senior SW Engineer, MCS, I`m not a robot [V], #opentowork
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Yeah, remember that big percentage of people are color-blind or have other vision problems. {this is just a dumb space to get to minimum number of chars :-)}

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Ryan T.

Engineering impactful and experiential solutions
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In this aspect, I do agree that colors, especially, needs to be used in a strategic manner. Just because it is there to be used, does not mean that it must be used. For example, if green is used to denote some positive trend, then it should be used consistently to represent that across the charts. At the same time, if there's a universal understanding that certain colors denotes specific emotions, then it could be used to amplify the story, e.g. green for positive feelings, red for negative feelings.

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3 Not labeling or annotating your chart

A third common mistake is to not label or annotate your chart properly. Labels and annotations are essential to provide context, clarity, and explanation to your data visualization. Without them, your viewers may not understand what your chart is showing, what the units or scales are, what the data sources or methods are, or what the key points or insights are. For example, without a title, your viewers may not know the topic or purpose of your chart. Without axes labels, your viewers may not know the variables or dimensions of your data. Without legends, your viewers may not know the meaning of the colors or symbols in your chart. To avoid this mistake, you need to label and annotate your chart clearly and concisely, and use appropriate formats, such as captions, tooltips, or callouts.

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4 Not checking your data or code

A fourth common mistake is to not check your data or code before creating your data visualization. Data and code errors can lead to inaccurate, incomplete, or misleading data visualizations that can damage your credibility and reputation. For example, data errors can result from typos, missing values, outliers, duplicates, or inconsistencies. Code errors can result from syntax, logic, or runtime errors that can affect the calculation, transformation, or display of your data. To avoid this mistake, you need to check your data and code carefully and thoroughly, and use tools and techniques, such as validation, debugging, or testing.

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Eliyahu Ravuna

Sr. Software Engineering Manager at Johnson & Johnson MedTech (Biosense)
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Did you ever want to get a simple number, asked teams, and received a different answer from every team? This is very common in big enterprises. In the best case, teams use different definitions, all answers are correct. In the worst case, teams have bugs. Enough resources shall be invested to develop and test systems to calculate a single version of the truth. These systems shall offer drill-down, to allow testing via random sampling. Design and code reviews shall be performed to minimize errors. And I also recommend an enterprise dictionary, so everyone understands the same thing when they hear the same number.

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5 Not getting feedback or evaluation

A fifth common mistake is to not get feedback or evaluation on your data visualization. Feedback and evaluation are important to improve the quality, usability, and effectiveness of your data visualization. Without them, you may not know if your data visualization meets your goals, meets your audience's needs, or follows the best practices. For example, feedback can help you identify and correct any errors, gaps, or inconsistencies in your data visualization. Evaluation can help you measure and optimize the impact, performance, or outcomes of your data visualization. To avoid this mistake, you need to get feedback and evaluation from different sources and perspectives, such as peers, experts, users, or metrics.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common mistakes when creating data visualizations?

1

2

3

4

5

6

1 Choosing the wrong chart type

2 Using too many colors, fonts, or effects

3 Not labeling or annotating your chart

4 Not checking your data or code

5 Not getting feedback or evaluation

6 Here’s what else to consider

System Architecture

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What are the most common mistakes when creating data visualizations?

1

2

3

4

5

6

1 Choosing the wrong chart type

2 Using too many colors, fonts, or effects

3 Not labeling or annotating your chart

4 Not checking your data or code

5 Not getting feedback or evaluation

6 Here’s what else to consider

System Architecture

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