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“Data, data, data, and some more data!”,- data is one of the most prominent terms that you come across these days. You hear it almost everywhere and, in every business whether it is healthcare, financial, sports, electronics, or climate. The revolution in the computerization and automation has not only made it easier to collect data but has also led to a multifold increase in the speed of data acquisition. This fact is beautifully captured in the following statement made by Eric Schmidt, ex-CEO of Google,
“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”
In the past two decades alone more data has been recorded than it had been in the previous centuries combined. However, the collection of this large volume of data about any system is meaningless if it cannot be put to use. The data can be considered as a collection of jumbled words describing a system. As such, when these jumbled words are deciphered and connected properly, they are capable of conveying interesting and meaningful messages about the system. Thus, in this era of big data it becomes critical to analyze data to gain knowledge, get new insights and make inferences about the system so that it can be used to improve the learning and living experience of the society as a whole.
Fig 1. Data analysis and visualization is critical to extract meaningful information [1]
I would like to use yet another quote to convey the importance of collection and analysis of data in today’s world,
“Information is the oil of the 21st century, and analytics is the combustion engine.” ~ Peter Sondergaard, Senior Vice President and Global Head of Research at Gartner, Inc
In several discussions it is said that the data has become even more valuable resource than oil for the world. In fact, with this comparison, it is often said that just like oil is of no use unless it is extracted and burned, similarly the data is useless unless it is exploited (read analyzed). One of the best ways to represent the information hidden in the data is to use data visualization.
Data visualization is a technique to represent data in the form of a visual story than a literal form. The visuals can be graphs, charts, plots, tables, or maps. The data visualization method is easier to comprehend as it gives a clearer picture of any correlations, trends, patterns, or distribution that may exist in the data than what can be inferred from a written report or a spreadsheet.
With the advent of big data and the big data analytics, there has been a boom in the market for tools and software that can be used to analyze and visualize data to gain deeper insights and meaningful inferences. Tableau is one such interesting tool.
‘Tableau’ is a French word that means to present a graphical description or a representation. The Tableau software true to its name is an interesting tool used for analyzing data to create compelling data visuals, generate reports, and identify key performance parameters that can tell a complete story about a business or system being presented.
Fig 2. Tableau Logo
However, it is interesting to know that Tableau is not a single product but a suite of products. The different Tableau products are as follows:
1. Tableau Desktop – It is a platform that people use to independently complete the data exploration, analysis, and visualization before communicating with others. The advantage of using a Tableau Desktop is that one can independently evaluate the data and save the analysis without having to publish it.
It is a paid service. The advantage of using Tableau Desktop is that it allows you to save your analysis in a workbook that in turn can be saved on a private environment (both on-premises and cloud-hosted).
2. Tableau Prep – It is a separate application that is used to extract, transform, and cleanse data before the visualization step. It works together with Tableau Desktop.
3. Tableau Server and Tableau Cloud – Tableau Server and Tableau Clous are used for storage of data analysis and visualizations so that they can be shared with the other end users at a later time. The difference between the two is that the Tableau Server requires the enterprise user to host the environment while the Tableau Cloud is a SaaS-hosted infrastructure managed by Salesforce.
4. Tableau Public – It is a free online platform used to share data, analysis, and visualizations with the public without a restriction. However, it has a few limitations compared to Tableau public – you can access a limited number of rows of data per workbook (~ 15 million), you cannot connect to any servers other than Tableau Public and you can only save data on Tableau Public which can be accessed easily by anyone so you cannot use any confidential data for analysis.
5. Tableau Reader – It is used to reference data source in packaged workbooks created in Tableau public for sharing with smaller groups in order to avoid accessing data source using an online download.
6. Tableau CRM Analysis - It is a single tool created by Salesforce by combing its Einstein Analytics Platform with the data management, preparation, and visualization functionalities of Tableau after it acquired Tableau.
7. Tableau Data Management – It is used to create a virtual connection for shared central access to points to disparate data sources thus, eliminating the need for creating a one-off connection for each data instance.
8. Tableau Advanced Management – It is used manage multiple datasets with additional layers of security.
Thus, depending on the need, purchase of multiple Tableau products maybe required.
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