data Visualization Pitfalls and How to avoid them

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Data Visualization Pitfalls and How to avoid them

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Data Visualization Pitfalls and How to avoid them

For many decades individuals have been using charts so that they can clearly comprehend business data. The presence of visuals makes complex data clearer but there are pitfalls that ruin the experience of data visualization and need to be avoided completely (Bouquin & Epstein, 2015). The pitfalls are color abuse, color in data visualization has its place but individuals have been overdoing it. Individuals have made the mistake of choosing the wrong color which leads to poor interpretation of data and confusions (Gemayel, 2018). Individuals do not take their time to analyze the color that conveys easy meaning. The best way to avoid abusing color is to take time and analyze the best color using brand colors and individuals should also avoid overreliance of data to convey meaning.

Another pitfall of data visualization is the misuse of pie charts. pie charts are used to make the understanding of data easy, individuals ruin the benefits of pie charts by squeezing too much information until it becomes hard to comprehend. Data in pie charts should be organized to avoid confusions. Pie charts work best when data can easily be understood (Stofer, 2016). To avoid making mistakes, one should organize data from largest to smallest (Stofer, 2016). Visual clutter is another pitfall that should be avoided, it happens when there is too much information hence ruining data clarity (Stofer, 2016). Individuals should avoid the use of unnecessary information that ruins the meaning of data. The less information there is the easy it is to make conclusions.

Poor design is another pitfall, the effective design should be beautiful on the eye but it should be simple to work with. The best design should easily communicate to the audience and make easy for them to comprehend the data (Gemayel, 2018). Individuals should use the help of qualified designers when coming up with effective visualizations. One should avoid creating visuals and dashboards for the sake of it, they should consider the interpreter of information (Bouquin & Epstein, 2015). Bad data is another pitfall that ruins the interpretation of information. to avoid submitting bad data, one should take time to analyze the data by doing so it becomes easy to spot data issues early and they should be addressed before presenting them.


Bouquin, D., & Epstein, H. A. B. (2015). Teaching data visualization basics to market the value of a Hospital Library: An infographic as one example. Journal of Hospital Librarianship15(4), 349-364.

Gemayel, R. (2018). How to design an outstanding poster. The FEBS journal285(7), 1180-1184.

Stofer, K. A. (2016). When a picture isn’t worth 1000 words: Learners struggle to find meaning in data visualizations. Journal of Geoscience Education64(3), 231-241.

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