The correct presentation of data is one of the most important tasks associated with processing information. Regardless of the area, the data should be presented accurately and not allow different interpretations. One of the convenient tools for implementing these principles is categorization. Using this method, one can concisely present the necessary data. However, even with all the necessary tools, many people still misuse them, deliberately or accidentally confusing the viewer. This essay aims to analyze examples of good and bad data representations to explain the principle of responsible information display and the benefits of categorization.
A person is faced with a massive amount of data every day, and it is growing from year to year thanks to the spread of technology. However, despite all the advantages that modern technologies provide, many people misuse them. Research shows that humans are the best at perceiving visual information (McCandless, 2010). That is why a graphical display of data in various comparison charts, visual illustrations and graphs works best. However, many people neglect such visualization, using solid text instead, full of complicated details.
This approach is the first example of bad use of data representation. Even if the information is categorized and presented consecutively, it is practically useless without using enough graphical data to allow visual comparison. Appendix A includes various examples of poor representation of information, and entry number 4 is a perfect example of categorizing information without simplifying it. Although the U.S. military budget is divided into all categories, the data is challenging to perceive and relate to each other. A similar problem is observed in items 1 and 2 of Appendix A. Despite the strict categorization, the available structure, and the data’s reliability, their value is questioned.
This is a problem common to all three examples – the texts use absolute values that have no connection with other factors. As a result, their perception is complex, and the data themselves do not reflect the actual state of affairs. Example 3 shows a graph of the change in U.S. Army personnel numbers depending on the year. Although these data are reliable, they do not show what the reasons for this phenomenon are. Despite the graphical representation of the data, the numbers shown are still absolute, and there are no comments as to what these numbers mean.
The presence of comments and additional information can significantly simplify the understanding of even complex information volumes with many intersections and additional links. Appendix B provides examples of good data representation, and as an example for this particular case, entries 1 and 5 are worth considering. Both articles are devoted to the spending of various countries on the military budget, which echoes the article discussed above. However, the information presented here is categorized, and has additional comments and links with indirect themes and causes; therefore, it is much closer to reality. Entry two, dedicated to forest losses, is an example of how the same statistics can be presented in different ways, expressed in numerical and relative forms.
Pie charts are a great example of using visual relationships between different categories. Although the graph from point 4 is of a different graphical type, it also provides a relative understanding of the nature of the information conveyed. Despite the complete absence of absolute values, the viewer can observe changes in the ratio of forest cover in different parts of the planet. However, absolute values can be used where there is no possibility of misinterpretation, such as in sports tables, an example of which is paragraph 3 of Appendix B.
Therefore, displaying information responsibly using categorization must meet the following requirements. In addition to the fact that all displayed data must be reliable, their use must exclude the possibility of ambiguity. In addition, from my perspective, the active use of graphic elements makes it easier to understand the information. It makes it possible to compare categories and demonstrate the dependencies between them visually. Therefore, categorization is an effective tool for displaying data but must be used with care and awareness of the indirect relationships that may affect perception.
Reference
McCandless, D. (2010). The beauty of data visualization [Video]. TED. Web.
Appendix A
Examples of sources of bad data representation:
- 100 of the most energy-efficient companies in America. (n.d). Electric Choice. Web.
- Clancy, H. (2014). 10 companies to watch in energy analytics. Forbes. Web.
- Duffin, E. (2021). Active-duty U.S. Army personnel numbers 1995-2019. Statista. Web.
- Macias, A. (2021). Here’s the firepower the Pentagon is asking for in its $715 billion budget. CNBC. Web.
- Weisse, M., & Goldman, E. D. (2017). Global tree cover loss rose 51 percent in 2016. World Resources Institute. Web.
Appendix B
Examples of sources of good data representation:
- Amadeo, K. (2020). US military budget, its components, challenges, and growth. The Balance. Web.
- Covid-19 and energy efficiency (2020). IEA. Web.
- English premier league table 2021-22. (2021). ESPN. Web.
- Global tree cover is expanding. (2018). Beautiful News. Web.
- Szmigiera, M. (2021). Countries with the highest military spending worldwide in 2020. Statista. Web.