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Tableau is one of the tools that helps in Data Analysis and Data Visualization. This tool helps to create a visual presentation to convey information which is easy to read and understand.
I have created a tableau presentation to show regions, cities and sub-categories that are generating losses or negative profits. I have also added Regional Managers who are leading those regions so we can let them know about the outcomes. I have used a sample superstore Dataset from Kaggle.com for Data Analysis and Data Visualization. You can use any data to create a similar report.
First, I have created a Bar Chart (Image 1), showing the trend lines. It gives an idea about whether a company is making a profit or a loss. The bar chart plotted is the Sum of Profit for the Order Date and as per Quarter. Colour shows the sum of Profit. The trend line shows an upward trend of increased profit with an average profit of $17800. However, data contains negative profit values as well. So, I have created another chart of losses per sub-categories as shown in (Image 1-a). Still, it is not clear which sub-categories are more in losses. Therefore, we need to analyze the data further to determine the losses.
So, I have created a pie chart (Image 2) which shows the region-wise sales, profit and profit %. I wanted to focus mainly on negative profit values or losses only. Therefore, I have filtered the profit values ranging from $0 to -$6600.
As shown in the Pie Chart (Image 2), major losses are incurred in the Central Region with 36%, followed by Eastern regions with 32%, the Southern and Western regions with 18% and 15% of overall losses. Now, I wanted to see losses in each Region and as per sub-category. So, I filtered the data on Profit, which ranges from -6599.978 to 0. The Bar chart (Image 3) shows the main sub-categories that contribute to high losses in the Central Region are Binders, Appliances, Furnishings and Chairs.
Similarly, I have created a Bar Chart (Image 4) for the Eastern Region as well. The main sub-categories which incurred high losses in the Eastern region are Machines, Binders, Tables and Phones.
Similarly, I have created a pie chart (Image 5 and Image 6) for Southern and Western Regions as well. The main sub-categories that generated negative profit are Binders, Tables and Machines in the Southern region. In the Western Region, major sub-categories are Machines, Bookcases, Tables and Binders, generating more losses.
As seen above, I can say that Machines, Bookcases, Tables, Binders, Phones, Appliances and Furnishings are major sub-categories that generated negative profit values.
Let's narrow it down and crosscheck to see which categories are in losses. As shown in a Text Chart (Image 7), sub-categories, Binders (25%), Tables (21%), Machines (19%) and Bookcases (8%) are top contributors of losses to the company.
Also, As per the Pareto Chart (Image 8), Chairs, Bookcases, Machines, Tables and Binders are the sub-categories that have generated 80% losses in almost all regions.
Further, I have focused mainly on the above-mentioned 5 sub-categories and narrowed them down to city-wise and region-wise. I have also added the regional managers who are leading those particular regions. I have also filtered the profit to below $1000 for the main sub-categories. So, as per the Text Chart (Image 9), the Central region contributes approx. $24000, Eastern Region Contributes approx. $20000 and the Southern Region Contributes approximately $6000 losses in overall losses in those sub-categories (Binders, Machines, Tables, Bookcases, and Chairs).
So, using the Map chart (Image 10), we can see stores located in cities (highlighted) have incurred high losses in Binders, Machines, Tables, Bookcases and Chairs sub-categories.
With the help of Tableau, we have analyzed and visualized the sample superstore data. We can say that company or regional managers need to focus on those sub-categories (Binders, Machines, Tables, Bookcases, and Chairs). that have resulted in high losses. We can further analyze the data as per cities, stores and sub-categories and identify the factors generating the high losses. Similarly, we can also focus on positive values of profit to analyze and visualize data for profit-making factors, trends and forecasts to generate more revenues.
I hope this sample project gave you a better insight into how Tableau can help in Data Analysis and Data visualization. I hope you enjoyed the post.