Data Visualization and Analytics Using Tableau

Paper Info
Page count 3
Word count 883
Read time 4 min
Topic Tech
Type Report
Language 🇬🇧 UK

Background Information

The present paper discusses how Tableau can be used for visualizing data for key stakeholders. The report uses data provided by Dr. Hans Hofmann (no date) downloaded from the archives of the University of California. The data was expected to be used during a marketing team meeting to create a demographic profile of the most profitable customers. Thus, the primary stakeholders are members of the marketing team, including the marketing director.

Used Charts

Four charts were used to represent data about the demographical characteristics of customers of the bank. The first chart demonstrates how the credit amount taken in the bank is distributed by age (see Figure 1). The bar chart was chosen as the most appropriate chart, as it is beneficial to cluster individuals into age groups. Bar charts show each data category in a frequency distribution. Additionally, they estimate key values at a glance and clarify trends better than other types of charts. However, bar charts often require additional explanation even though they are widely used in the business area. Bar charts can be substituted by other types of diagrams, including packed bubbles, pie charts, and treemaps. However, in this situation, the bar chart was the most appropriate, as it helped to understand the trend in the total credits amount.

Credit amount against age.
Figure 1. Credit amount against age.

The second chart demonstrates the credit amount distribution by personal status and sex (see Figure 2). The selected chart was the pie chart, as it helps to understand the distribution share of people of different ages from the total number of customers. Pie charts are familiar to the majority of viewers, and they are visually simpler than other types of graphs. Thus, they require minimal additional explanation if they are not overloaded with the information. However, pie charts do not easily reveal exact values and fail to demonstrate the trend in the distribution of values. Pie charts can be partially substituted by bar charts, packed bubble charts, and treemaps. However, all these types of diagrams make it difficult to understand how much a category contributes to the entire population. Thus, the pie chart is the most appropriate diagram for the presented data.

Credit amount against sex and personal status.
Figure 2. Credit amount against sex and personal status.

The third chart demonstrates the distribution of credit amounts by checking account statuses of customers (see Figure 3). The chart was expected to answer the question if the marketing team should target current checking account users. The most appropriate chart type here would be a pie chart. However, it was decided to diversify the chart selection and use a treemap. Even though treemaps are similar to pie charts in their purpose and can help the viewers read the exact amount, treemaps work best when it is required to present information on many variables. Additionally, they often require additional explanation.

Credit amount against checking account status.
Figure 3. Credit amount against checking account status.

The fourth chart visualized the distribution of credit amounts by types of jobs (see Figure 4). The selected chart was packed with bubbles, as it helps to compare the groups in a comprehendible manner. The most appropriate chart for the purpose would be a pie chart, and the selected style was chosen for diversification purposes. The central problem is that the packed bubbles chart does show the proportions and does not easily reveal exact amounts.

Credit amount against job titles.
Figure 4. Credit amount against job titles.

Dashboard Creation

Building dashboards is crucial for all the stakeholders, as they help to inform the viewer about the key insights from data at a glance. Tableau lets even non-technical users create interactive dashboards that help stakeholders make data-driven decisions. However, it is crucial dashboards do not overload the users with information and provide a chance for further investigation. Figure 5 was an attempt to create a dashboard that could help all the members of the marketing team understand the demographics of the customers and act appropriately.

Figure 5. Dashboard.

The dashboard is entitled “Demographic characteristics of customers,” as it helps to grasp key characteristics of customers, including ages, sex, marital status, job titles, and checking account statuses. All four charts were included in the dashboard because they fit a common theme. Since all of the charts have similar purposes and importance, each of them was given an equal amount of space in the dashboard. The dashboard provides the viewers with the ability to navigate between charts and download a PPT or PDF version of the presentation. These functions are expected to be used frequently for disseminating information.

While the dashboard is functional and informative, it seems to lack interaction and outside information. It was initially supposed to add external websites that provide additional information on the topic. However, the websites did not fit with the design of the dashboard, as they drew too much attention and provided little information. Additionally, it was initially supposed to add a link to the dataset for the stakeholders to download it and conduct quantitative analysis. The idea was dismissed, as it would overload the dashboard with a function that would be used rarely. Another flaw of the dashboard concerns the navigation buttons, as they seem too large and do not fit the overall color design of the dashboard. Despite the mentioned flaws, however, the dashboard serves its purpose and can be used for disseminating crucial information.

Reference List

Hoffman, H. (no date) German credit data. Web.

Cite this paper


EssaysInCollege. (2022, May 24). Data Visualization and Analytics Using Tableau. Retrieved from


EssaysInCollege. (2022, May 24). Data Visualization and Analytics Using Tableau.

Work Cited

"Data Visualization and Analytics Using Tableau." EssaysInCollege, 24 May 2022,


EssaysInCollege. (2022) 'Data Visualization and Analytics Using Tableau'. 24 May.


EssaysInCollege. 2022. "Data Visualization and Analytics Using Tableau." May 24, 2022.

1. EssaysInCollege. "Data Visualization and Analytics Using Tableau." May 24, 2022.


EssaysInCollege. "Data Visualization and Analytics Using Tableau." May 24, 2022.