A guide to Data visualization using Tableau

We are surrounded by data, and the ability to effectively visualize data and draw reasonable insights from data is very crucial. Tableau is a common name that pops out when discussing data visualization. Tableau is a powerful tool in the data visualization domain and it is empowering users to transform real-time complex data into visual stories, across many industries. So, let's dive into what Tableau is, its capabilities, the charts that Tableau offers, and how one can select what chart can be used for data visualization.

Introduction:

Tableau software was founded in 2003 by Christian Chabot, Pat Hanrahan, and Chris Stolte, who were alumina of Stanford University. They aimed to make data visualization available to people across domains which further helps in understanding data better. Over the years, Tableau developed innovative products such as the Tableau desktop, which helps users explore data by creating visualizations and these visualizations can be posted in Tableau public, where people across regions can view them. Tableau Server Online, and Tableau Prep are a few of the other services that Tableau has developed. Over the years, Tableau evolved and emerged to be one of the top players in Data analytics. In 2019, Salesforce acquired Tableau expanding Tableau's reach to users and enhancing its integration capabilities.

Overall, Tableau is a powerful data visualization tool that helps users create interactive dashboards, and visualizations which are both sharable and interactive. Say, if one is analyzing the usage of technology by gender, one can select only one particular gender during presentation to the end user.

 The dashboard of Tableau is shown below:

Features:

The unique features of Tableau that make it user-friendly and popular are

·       Tableau is flexible in connecting to a wide range of data sources like SQL servers, MySQL, Oracle, APIs, and

spreadsheets like Excel, and CSV.

·       Tableau involves dragging and dropping interface to create colorful visualizations, where we can change the

formatting to make it more appealing and useful for viewers to understand data better. This created

dashboard could be shared easily through Tableau online which could be worked on further.

·       Uses less coding, so anyone without former coding experience could use it. There are inbuilt calculation

fields, parameters, and table calculations that allow users to do complex analyses instead of applying

calculations on these data outside Tableau and then importing the data to Tableau for analysis.

·       Mobile applications that make dashboards accessible and interactive on smartphones and tablets, where

dashboard sizes are optimized as per screen size.

·       Tableau can be integrated with R and Python languages for advanced analysis.

Charts:

In general, a chart is a visual representation of data in the form of a graph, diagram, map, or tabular format that enables the user to understand the data better.

The data is visualized on the chart by plotting data points using cartesian coordinates like x,y, and z based on a set of dimensions and measures where each data point is plotted based on its values along them, in short, the measure of analysis are grouped along dimensions coordinates to create the visualization. Dimensions are categorical fields that cannot be aggregated, whereas measures are numerical fields that can be measured, and aggregated.

Once a dataset is loaded in Tableau, Tableau automatically divides the data into dimensions and measures. You can see this in the workspace area under the data pane.

Tableau offers 24 charts under “Show Me”. Some of these charts could be combined further for better visual analysis. The most common charts like Bar Charts, Line Graphs, Scatterplots, and Pie charts. These charts alone or with a combination of other charts provide visual insights to most questions dealing with relational data. Some charts display many dimensions while other charts can support only a few with clarity.

Visualization Families:

These charts can be grouped under 4 different visualization families based on their primary use and functionality of each chart type.

o   Charts:

Most common and effective graphic visualization for data. Bar chart, Line chart,

Area charts, Scatter plots, Pie charts, Histograms, Box plots, Bullet charts, side-by-side bar charts, Bubble charts, Gantt charts, and Stacked bar charts come under these categories.

o   Geospatial:

This group includes maps and other visualizations that plot data on a geographic background. This includes Geographic heat maps, tree maps, point distribution maps, symbol maps, and density maps which are plotted on geographic backgrounds.

o   Analytical:

This group includes advanced visualizations which can be used for advanced analysis.

Heatmap tables, Box-and-whisker plots, trend lines, reference lines, forecasting, and clustering are included in this category.

 o   Tables:

This category includes tables and other formats that display data in a grid layout. Text table comes under this category.

Some of these charts can fall under two different visualization families, as these graphs are versatile and can show different aspects of data depending on context and analysis goals.

Below are how charts are displayed on the Tableau screen, side table shows the information of the details of the chart in brief.

Chart Selection:

Selecting specific kinds of charts for analysis depends on the data and the insights that one wants to convey to the audience. The chart selection criteria can be summed below:

·         The type of data you have, the variables could be Quantitative, Categorical, Temporal, and Spatial.

• Based on the relationship between variables like comparison, composition, and distribution of variables, chart type can be selected.

• Based on the purpose of visualization, which could be to show the trend, comparison, composition, relationship, geography, and time change of ‘or’ between variables.

• Based on the purpose of the visualization, is one going to show the variable's trend, comparison, composition, change over time, relationship, and geography?

• Also depends on the audience and their familiarity with data visualizations

So, in summary, chart types are specific visual representations, while visualization family’s group related chart types together based on their purpose and characteristics. Choosing the right chart type depends on the data and insights you want to convey while selecting the appropriate family depends on the overall analysis goals.

Conclusion:

Data visualization is an art and science, Tableau has revolutionized the way it is looked upon, due to its robust data connectivity, extensive range of chart types offerings and visualization families, and user-friendly interface. Using them, one can unlock data’s full potential to create valuable insights that are helpful across varied industries and domains. One could be a proficient data scientist or a beginner to explore this tool and experiment with different visualization techniques and create meaningful insights from raw data that enable the business to understand the relationships, trends, and patterns between variables and proceed further.

*Dashboard image from my Tableau public.