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Data visualization entails presenting information or data in a visual way. The aim is to make the information clear and effective to the readers and viewers. According to Alnjar (2017), data visualization improves the narrative form of information and data. Data is usually presented in the form of tables, maps, charts, and graphs, through which people can quickly extract the meaning. Gatto (2016) states that creative data visualization can be nice to look at. However, the presentation ought to be functional as well in its graphic communication of the information and data.
Data visualization can be effective if it is able to break down a large set of figures into a simple and digestible form. The figures can be presented in the form of charts; a scatter plot or a graph that can easily show the connection between many variables in a single view (Szafir, 2018). It also should be able to break down complex concepts in a simple graphic form for the layman to understand what otherwise could have been a difficult concept. Therefore, charts with so much information humped up together need to be avoided.
Moreover, data visualization should be able to tell a story from within the data. A chart can easily evoke some emotions by giving meaning and liveliness to data. For instance, the use of different color themes during the presentation and different styles of design brings out some emotional appeal to the intended audience. People also believe more about what they see rather than what they are told (GoodData, 2020). Data visualization should therefore help to push an agenda by creatively using the available tools to show the strengths and weaknesses of an idea or compare different theories or views. The tools such as contrasting colors, symbols, and shapes should also enable the presenter to stimulate the readers towards a particular conclusion (Larson et al., 2017). Data visualization should make the presentation as engaging as possible by challenging the brain.
On the other hand, data visualization can go bad in various instances. This can occur if the presenter puts a lot of information in a single chart for presentation. In addition, data visualization can go wrong if the wrong type of chart is chosen instead of the correct one. There are different types of charts that suit different types of data. Therefore, proper consideration of the correct type is crucial for successful presentation (Data Ranger, 2020). In addition, different types of charts are designed to produce certain types of visualization only. The bigger audience may not be able to grasp the intended meaning unless it is again explained in detail to them. The use of these types of charts must be minimized, especially for the general audience.
Data visualization can be costly to produce, which discourages its use across different platforms. For instance, some artworks are known to cost millions of dollars which is out of reach of the larger population. According to Pandey et al. (2019), the improper use of the latest technology, such as 3D graphics, can lead to false interpretations if not used properly. Some visualization can end up appearing larger or smaller than the real description, and this can cause the presented information to be skewed. Data visualization is also hampered by the use of flashy rather than using the appropriate form of visualization (Bowers, n.d.). Such practices may have an impact on the prospect of making critical decisions that affect the larger part of society.
References
Alnjar, H. Analysis and synthesis of critical design-thinking for data visualization designers and learners; Data visualization metrics between the theoretical view and application. [Internet]. Researchgate.com. 2017. Web.
Bowers, M. Numbers shouldn’t lie – An overview of common data visualization mistakes. [Internet]. Toptal.com. n.d. Web.
Data Ranger. Making research useful: Current challenges and good practices in data visualization. [Internet].Web.
Gatto. M.A.C. Making research useful: Current challenges and good practices in data visualization. [Internet]. Web.
GoodData. 5 Data visualization best practices. [Internet]. GoodData.com. 2020. Web.
Larson, A.M., Freeman, T.E., Ringer, R.V., and Loschky, L.C. The spatiotemporal dynamics of scene gist recognition. [cited 25 June 2021]. Journal of Experimental Psychology: Human Perception and Performance. 2017; 40(2) 471.
Nediger, M. What is Data Visualization? (Definition, Examples, Best Practices). [Internet]. Web.
Pandey, A.V., Rall, K., Satterthwaite, M.L., Nov, O., and Bertini, E. How deceptive are deceptive visualizations?: An empirical analysis of common distortion techniques. Precast of the ACM Conference on Human Factors in Computing Systems. ACM, New York, 2019.
Szafir, D.A. The good, the bad, and the biased: Five way visualizations can mislead (And how to fix them). [Internet]. Web.
Szafir, D.A., Haroz, S., Gleicher, M., and Franconeri, S. Four types of ensemble coding in data visualizations. Journal of Vision 2016.
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