Tableau is a powerful data visualization tool that can be used to reveal patterns in Big Data. The visualization tool compares patients who recovered or died from Covid-19 in the different states within the country. The visualization suggests that states with the highest number of confirmed deaths have low numbers of recovered patients, for instance, New Jersey (NJ). On the other hand, states with high numbers of recovered patients have low numbers of confirmed deaths, for instance, Texas (TX). The exercise demonstrates that Tableau is a powerful data visualization tool that can convert vast data sets into simple visualizations that are easy to understand. Visualization gives decision-makers a clear idea of what Big Data means by giving it visual context through graphs and maps. For instance, decision-makers can use the visualization to identify states with the highest number of confirmed deaths. They can then proceed to develop policies that can help reduce deaths in these states. Decision-makers can decide to offer more personal protective equipment (PPE) and ventilators to healthcare institutions in struggling states. Policy-makers can make it mandatory to wear face masks while out in public and encourage citizens to practice social distancing to reduce the likelihood of contracting the virus. Visualization tools such as Tableau create graphical illustrations that allow decision-makers to draw insights and make informed decisions on critical issues.
Use of Tableau in a Professional Environment
Data visualization tools have developed as critical assets in the professional environment with the power to change the way Big Data is accessed, presented and used. Tools such as Tableau present accessible ways to see and comprehend patterns, correlations, and outliers in data using graphic elements such as maps, graphs, and charts. Data visualizations tools have different uses within the professional environment. Visualizations tools like Tableau are used in finance to analyze an organization's financial health and present analytic insights to the broader company through graphs and charts. Tableau can help finance analysts identify insights and additional trends using interactive features or multiple data sources. Visualization tools can also be used in marketing. Tableau is used in marketing departments to offer an easy, swift, and effective way to identify significant patterns that are vital in the marketing. Data visualization is used to highlight the patterns in consumer behavior. For example, it can illustrate how price changes affect purchase behavior. Visualization is essential as it allows the sales and marketing team to gain insights and make quick decisions in a competitive business environment ( Umesh & Kagan, 2015 ). Tools like Tableau are significant in the professional environment as they increase efficiency by streamlining the decision-making process. Visualization has also helped companies become more competitive because they base their decisions on valuable visual elements.
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Benefits of Big Data Visualization
Big Data visualization can present several benefits to an organization; they include enhanced ad - hoc data analysis, improved decision-making, better information sharing and collaboration inside or outside the company. Ad - hoc data analysis allows analysts to gain insights from a smaller section of data on an as-needed basis to offer a single business query solution. Visualization techniques offer geometric modelling and extraction, which reduce the actual data size before rendering to improve analysis (Olshannikova et al., 2015). Big Data visualization also enhances decision-making. Visualization offers insight from various business perspectives. It offers a better understanding of data and allows decision-makers to look at the association between different variables, for example, data on customer-company relations, consumer behavior, and the correlation between products, sales, and consumers (Olshannikova et al., 2015). Obtaining accurate, consistent, and relevant information results in higher efficiency in the decision-making process. Big Data visualization facilitates better information sharing and collaboration inside and outside the company. Visualizations offer a simple way of conveying information in Big Data as visual representations that are easier to understand. Visual illustrations such graphs present data that is easily conceivable to the general public, which fosters better collaboration since people have a better idea of the issue discussed. Big Data visualization can help improve different aspects of an organization.
Risks of Big Data Visualization
While there are several benefits of Big Data visualization, it also has several potential risks. One risk associated with Big Data Visualization is cryptic encoding and confusion. The visual format used to depict data may not be globally understood, which can confuse the viewers. Additionally, visualizations that do not have accompanying text or clear overall logic may confuse some of the audience (Bresciani & Eppler, 2015). Another risk of Big Data Visualization is the lack of skills of the staff performing Big Data analysis. Data analyst must take time to practice to develop skill sets required to produce good quality visualizations ( Joint Information Systems Committee, 2013 ). Additionally, visualization has a high prerequisite for diagram interpretation. In most instances, a visualization's efficacy is dependent on the user's visual literacy and previous experience. Another critical threat is the lack of understanding of Big Data and the business it relates to. It is impossible to produce a meaningful visualization without a good understanding of the company, its data, its structures and annual cycles.
Another critical risk of data visualization is ambiguity. Ambiguity may arise when visual notations contain unlabeled symbols, which makes visualizations hard to interpret. Another critical risk associated with Big Data is inconsistency. Visual elements can make inconsistent use of different symbols, for instance, changing their meaning or function without signaling the change (Bresciani & Eppler, 2015). Another critical risk that affects Big Data visualization is the possibly misleading perception of a visualization's dependability. Some visualizations can appear more sound or convincing than they are. Additionally, some visualizations offer implicit (multiple) meanings, which results in ambiguous interpretations. Some visualizations have allusions that are not comprehensively explained or described and may be misinterpreted or go unnoticed. It is important to address these risks to improve Big Data visualization.
References
Bresciani, S., & Eppler, M. J. (2015). The pitfalls of visual representations: A review and classification of common errors made while designing and interpreting visualizations. Sage Open, 5(4) 1-14.
Joint Information Systems Committee (JISC). (2013 November, 6). Potential risks related to data visualization . https://www.jisc.ac.uk/guides/data-visualisation/potential-risks-related-to-data-visualisation
Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. (2015). Visualizing Big Data with augmented and virtual reality: challenges and research agenda. Journal of Big Data, 2(1), 22.
Umesh, U. N., & Kagan, M. (2015). Data visualization in marketing. Journal of Marketing Management , 3 (2), 39-46.
