Data Visualization: An Overall Financial Analysis of Tesla

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Table of Contents

Meaning of Data Visualizations
John Snow’s Observations
Modern Data Visualization Techniques
Scientific Publication Similar to John Snow’s Observations
Edward Tufte’s Approach
Data Visualization in the Field of Business
Conclusion
References

Data visualization is crucial in helping the audience understand complex information that is represented using numbers. Through data visualization, people interact with and see complex information in a simplified manner. John Snow pioneered the use of mapping for data visualization. Currently, technological advancements have revolutionized data visualization. Researchers adopt Edward Tufte’s approach to graphical excellence to visualize numerical and non-numerical data. Various business publications integrate data graphical techniques to make their data appealing and easy to understand. Data visualization is important for the business organization since it helps in gaining insights from vast amounts of data, promoting effective decision-making.

Meaning of Data Visualizations

Data used in research articles can be complex to gain meaning from it. Moreover, some researchers can use vast amounts of data that can become challenging to a targeted audience. Therefore, there is a need to present the data in a manner that any person with little knowledge can make meaning out of it (Park et al., 2021). Data visualization is a technique that researchers adopt to represent data and information through the use of common graphics (Park et al., 2021). Charts, plots, infographics, and animations, among other graphics, are commonly used. Charts such as line graphs help in the identification of trends of a given data. Data visualization has made research activities easy since it simplifies the data collected.

John Snow’s Observations

John Snow was a doctor who thought it fit to represent the information about Cholera attacks in England through mapping. Therefore, for several years, he followed the devastating cholera waves hitting England from time to time (Pelling, 2022). As the disease got more dangerous, John Snow made two significant observations. He noticed that the “miasma” theory that was developed by writers and researchers was misleading. John Snow observed that although entire households were wiped out by cholera, some remained unaffected. Moreover, he observed that vomiting and diarrhea were the common symptoms at the onset of the disease. Therefore, he believed that diseases were caused by something that was eaten by the affected individuals.

The cholera attack in the overcrowded Soho district of London initiated the use of mapping to record pandemics results. John Snow diligently recorded the cholera deaths as they mounted. He drew a map, on which he recorded the location of deaths on each street. For ease of interpretation of his recorded data, he used a black bar to represent each death. Where multiple deaths occurred at a similar address the bars were stacked on top of each other (Pelling, 2022). Therefore, one could easily tell the number of deaths on each street by counting the black bars. John Snow used black discs to represent water pumps’ locations. John Snow’s map was a crucial tool in understanding the spread and severity of cholera.

Upon observing the map various conclusions were made, providing significant medical evidence. Firstly, John Snow observed that the deaths were roughly clustered around Broad Street Pump. Secondly, there were scattered deaths in houses that were several blocks away from the Broad Street Pump. Thirdly, there were no deaths recorded two blocks east of the pump. Lastly, John Snow observed that their more deaths around Rupert Street Pump, and the residents in that area used Broad Street Pump. The map helped John Snow to establish that water supply cleanliness was key in controlling cholera. John Snow’s observations were an explicit example of the importance of data visualization.

Modern Data Visualization Techniques

Technological developments have led to the advancement of data visualization techniques. Therefore, there is a significant improvement in contemporary data visualization compared to John Snow’s era. The modernized techniques have made visualization easy for recording and interpretation. Firstly, there is the use of digital computers to draw and share maps (Park et al., 2021). Unlike John Snow, where hash marks were hand-drawn, computers have integrated artificial intelligence for map drawing. Consequently, there are minimal chances of errors with improved accuracy. For instance, an article by Skov (2021) shows the distribution of some keywords in various subjects through mapping.

Secondly, modern mapping has included visuals that are more appealing than that used by John Snow. The improved visualizations make it attractive to read and interpret the data that is being represented. For example, research by Hiatt et al. (2022) uses a combination of scatter, line, and bar graphs to show HIV-host interactions in primary human T cells. Lastly, modern maps have integrated predictive features through the use of extrapolation techniques. Therefore, it is easy to predict the future occurrence of a given scenario by observing the visuals used. The use of technology has made modern data information visualization easier than that during John Snow’s time.

Scientific Publication Similar to John Snow’s Observations

Simpson et al. (2022) made observations telling a similar story to John Snow. The researchers utilized data information visualizations to correlate the cholera outbreak and war in Yemen between 2016 and 2019. The researchers use mapping to record the frequency of war events in various areas in Yemen. Moreover, a map of cholera rates in similar areas was drawn for interpretation and correlation (Simpson et al., 2022). The researchers found that their cholera rates were high in regions with high clusters of war and traveling waves. Therefore, Yemen civil war, 2016-2019, exacerbated the cholera spread.

Edward Tufte’s Approach

Edward Rolf Tufte, an American statistician, laid out several approaches that can be adopted when representing data through visuals. Edward Rolf Tufte’s approaches are focused on graphical integrity, data ink, chart junk, and data density (Ng, 2022). For graphical integrity, Tufte suggested that visual representations must tell the truth, without overstating the effect. For data-ink, Tufte suggested that graphical representations must maximize data-ink and non-data-ink (Ng, 2022). For the chart junk, Tufte discouraged the use of excessive and unnecessary graphical representations. Furthermore, Tufte required the graphical representations to be proportional to the data being represented. Edward Tufte’s approaches to data information visualization have led to the use of meaningful graphics by researchers.

The data visuals used by Skov (2021) and Hiatt et al. (2022) conform to Edward Tufte’s approaches. The graphical representation used in the article by Hiatt et al. (2022) has borrowed data from previous research on a similar topic, achieving data integrity. Additionally, the graphs used have minimized the use of non-data-ink, instead using colors for data only. Meanwhile, the map used by Skov (2021) shows staff distribution, using black dots, proportionally. Furthermore, in both articles the researchers have represented necessary data, avoiding chart junk. Applying Edward Tufte’s approach to data information has made the research by Skov (2021) and Hiatt et al. (2022) easily interpreted.

Data Visualization in the Field of Business

Data visualization and representation serve various importance among business organizations. Firstly, data information helps business organizations to analyze data in better ways. The visual mediums help stakeholders understand key areas needed for improvement and maintenance. For instance, research by Fang (2022) used a graph to represent the market performance of the automotive industry compared to others. The data show that automotive is more profitable, allowing investors to focus more on the industry than others.

Moreover, data information visualization can help ease track of a business’s past performance. The use of line graphs makes it easy for a business analyst to understand whether a company can be potentially profitable based on its past performance. In the research by Huang et al. (2022) a line graph of the value of Tesla’s stock price from 2020 to 2024 has been used. The graph shows that the company’s stock price has been increasing its value for the period represented. Further, the use of data visualization in the paper has helped predict the price of Tesla’s stock for the year 2024. Therefore, data visualization is crucial for important decision-making among businesses and their stakeholders.

Conclusion

Although research data can be complex, the use of graphical representations makes it easy for their interpretation. Visual information representation involves the use of graphics to represent data that would otherwise be complex if described using text. John Snow is one of the ancient researchers to use mapping to track the cholera outbreak in England. Unlike the John Snow era, modern data visualization is associated with the use of advanced technology. Consequently, there is increased accuracy in representation. Additionally, Edward Tufte developed approaches that researchers can adopt to ensure that their visuals are appealing and make sense. Data information visualization is crucial for decision-making among various sectors including business organizations.

References

Fang, J. (2022). An overall financial analysis of Tesla. Advances in Economics, Business, and Management Research. 656, 1233-1238. Web.

Hiatt, J., Hultquist, J. F., McGregor, M. J., Bouhaddou, M., Leenay, R. T., Simons, L. M., Young, J. M., Haas, P., Roth, T. L., Tobin, V., Wojcechowskyj, J. A., Woo, J. M., Rathore, U., Cavero, D. A., Shifrut, E., Nguyen, T. T., Haas, K. M., Malik, H. S., Doudna, J. A., & May, A. P. (2022). A functional map of HIV-host interactions in primary human T cells. Nature Communications, 13(1), 1752. Web.

Huang, Z., Qi, S., & Sun, Z. (2022). Analysis Tesla in the future by binary option and four different sensitivities. Advances in Economics, Business, and Management Research. 648, 683-688. Web.

Ng, Y. M. M. (2022). Using data visualizations to study digital public spaces. First Monday, 27(4). Web.

Park, S., Bekemeier, B., Flaxman, A., & Schultz, M. (2021). Impact of data visualization on decision-making and its implications for public health practice: a systematic literature review. Informatics for Health and Social Care, 47(2), 1–19. Web.

Pelling, M. (2022). Mythological endings: John Snow (1813–1858) and the history of American epidemiology. Centaurus, 64(1), 231–248. Web.

Simpson, R. B., Babool, S., Tarnas, M. C., Kaminski, P. M., Hartwick, M. A., & Naumova, E. N. (2022). Dynamic mapping of cholera outbreak during the Yemeni Civil War, 2016–2019. Journal of Public Health Policy, 43(2), 185–202. Web.

Skov, F. (2021). Science maps for exploration, navigation, and reflection—A graphic approach to strategic thinking. PLOS ONE, 16(12), e0262081. Web.

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