How To Create Interactive Public Dashboards And Storylines In Tableau?
The tabular data will not give us as much information as it contains; the messy format and large numbers of entries make it difficult to do further analysis. So here comes the birth of different data visualisation tools and techniques. Data visualization is the art of presenting data in different graphical charts so that non-technical people can understand it easily. Using a perfect combination of elements like colors, dimensions, and labels can create a masterpiece of visual reports that can reveal surprising insights, making businesses more growth. An increase in data analytics and data integration has made way for more specialized visual analytical tools. Typically files like excel spreadsheets are very good with analytics and visualization, but it has limitations like it can not handle big data, which is our main concern. On the other hand, specialised software leverages easy operation on both static and dynamic data, computational speed, self-service function, and interactive visualization facilitate users to pull up a report or dashboard or storyline and freely deep dive to granular levels of information. Tableau is one of the most used data visualization tools used in all industries, which helps o create interactive graphs and charts in the form of worksheets and dashboards to gain hidden insights easily. All this is made simple with gestures like drag and drop. Today, we mainly discuss creating an interactive dashboard and storyline for a given problem statement in this article. Problem Statement: A superstore is a very large supermarket which often sells household goods, clothes, electrical goods, and office furniture. So we need to find weak areas and strong areas where we can increase the profit. The dataset is available in CSV format, and you can download it from here. To work with tableau, you need to install it first. There are three versions of tableau, namely Public, Desktop, and online, out of which the Public version is available for free and for this tutorial, we are using it. I recommend this article if you are new to tableau; here, you will get a clear idea for installing, loading the data and creating the charts. Our dataset contains 13 attributes which contain information such as state, region, total sales done, total profit obtained, and so on. You can take a look at the spreadsheet and look at our dataset below; Load the worksheet: As the format of the data file is CSV, we need to load it as a text file from the home page of the tableau interface and later open a blank sheet from the bottom menu; you will get the interface like as below; Tableau categorizes the dataset’s attributes majorly in two types, i.e. Dimensions (inside the black box) and Measures(inside the brown box), which means categorical and numerical/continuous variables. Carefully observe the above, specifically the highlighted part of the image, as we will spend most of our time on this interface. We will place those attributes at the columns tag that we want to place on the x-axis similarly to rows tag variables that we want to display on the y-axis. Fields insides the red box Marks and filters are used to apply color combination, display the count values at the top of the graph, filter out unwanted information from attributes. From the show me tab (inside right-sided red box), you can control the type of plot that you want to apply; you don’t need to choose those plots as you drag and drop the attributes to the column and rows filed the plot will be automatically generated based on the type of attributes. You can navigate between sheets and create new sheets, dashboard and story sheets from the orange bottom box. We will now create four plots, i.e. map view of the US, the quantity being sold per category and sub-category, profit per category, and total price per sub-category. Lastly, I will create a dashboard and storyline. Map view of US: To create a map view, we need to drag the country and drop it on the sheet. This will generate a map view automatically. Secondly, drag the state attribute and drop it at the Label option present under the Marks field; this will highlight different states’ names on the map. You might observe that two fields are generated under continuous attributes, i.e. Latitude automatically and Longitude; we can use any one of them to represent states with the meaningful color combination.  We can simply make this by dragging the latitude and dropping it to the color option. That’s it all of these operations will generate the map as shown below; Quantity being sold per category and per sub-category: Drag and drop categorical attributes Category and sub-category into column tags and the continuous attribute Quantity into the rows tag. This will create a bar chart representing the quantity being sold in all categories. To represent sales done by these categories, we can move the continuous attribute Sales into the color option; this will represent sales and quantity being sold for each category in a more meaningful way. From the legends at the right corner, you see the meaning of colors. Profit per category: Drag and drop Subcategory and Profit into columns and rows, respectively; this will create an automatic bar plot again. To show the profit at each bar, drag and drop profit into the Label option under the Marks field. Finally, make a meaningful color combination drag and drop Category into color. If you hover your cursor to any of the bars, this shows the information like which Category and Sub Category it belongs to and the numeric value of profit for that bar. You can see this from the image below; Total price per sub-category: We don’t have attributes containing information on the price for each subcategory present in the field. So, in that case, we can create a new attribute for this by dividing the existing attributes Sales by the Quantity. Now the attribute we wanted now just drag and drop the attribute we created into rows and subcategories into columns. This will create a bar plot as shown below; Now we created our four plots as we needed; let’s move to the dashboard; Creating Dashboard: The dashboard is nothing but the consolidated display, which combines many worksheets and related information in a single place. The main purpose of this is to monitor and variety of data simultaneously. All the plots are displayed at once. To do this, we have to open a blank Dashboard, and there you have to drag and drop the worksheets you need to display.  And finally, to make it interactive, you have to filter the action from the Dashboard option present at the main menu bar. Finally, the dashboard will look like this; Creating Storyline: In general, the story in Tableau is nothing but a collection of different dashboards to show the sequence of events or visualise the main aspect of the analysis. We can create the storyline in the same way as we created the dashboard. Open a blank story sheet drag and drop dashboard you have created, then make duplicates of it and create a story as shown below; Conclusion: So this was all about creating dashboards and a storyline. Creating an interactive dashboard and storyline makes a huge difference when presenting your report at some work or in conference. Unfortunately, Tableau Public doesn’t provide a facility to store this work in a local machine. You need to save it in a tableau account, and there you can see all these plots and the dashboard. I have saved the Dashboard and Storyline at the tableau account; you can check those. References: Tableau Documentation
An increase in data analytics and data integration has made way for more specialized visual analytical tools. Typically files like excel spreadsheets are very good with analytics and visualization, but it has limitations like it can not handle big data, which is our main concern. On the other hand, specialised software leverages easy operation on both static and dynamic data, computational speed, self-service function, and interactive visualization facilitate users to pull up a report or dashboard or storyline and freely deep dive to granular levels of information.
An increase in data analytics and data integration has made way for more specialized visual analytical tools. Typically files like excel spreadsheets are very good with analytics and visualization, but it has limitations like it can not handle big data, which is our main concern. On the other hand, specialised software leverages easy operation on both static and dynamic data, computational speed, self-service function, and interactive visualization facilitate users to pull up a report or dashboard or storyline and freely deep dive to granular levels of information. 
["Big Data", "Data Analytics", "Data Science", "Tableau", "tabular data", "Visualization"]
["big data", "Go", "Tableau", "programming_languages:R", "AI", "Visualization", "programming_languages:Go", "tabular data", "Git", "RAG", "analytics", "Data Analytics", "Data Science", "Big Data", "R"]
["AI", "analytics", "RAG", "R", "Go", "Git", "big data", "programming_languages:R", "programming_languages:Go"]
Vijaysinh is an enthusiast in machine learning and deep learning. He is skilled in ML algorithms, data manipulation, handling and visualization, model building.