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Understanding the Tableau Jargon

Tableau is a software that has combined capabilities for data mining, data analysis, data visualization, data science, and business intelligence. The beauty of this software lies in its ability to allow users to analyze big volumes of complex data with just a few clicks on a simple to use graphical user interface. It aids in analyzing and presenting the convoluted data in the most simplified yet impressive visual data stories that can be understood by a wider audience. Thus, it can enables the users to make informed business decisions and convey them to the others.

As with any data platform, Tableau also has its own terminology. Some of the terms are common to any data tool while some are specific to Tableau. In the following sections we will talk about the jargons used in Tableau.

Data Source - As the name suggests, the data source refers to any origin of the data to be extracted by Tableau for analysis. The data sources in Tableau can be classified into the following four types,

1). Files- The data is stored in files that can be text files or excel spreadsheets or comma separated values files

2). Relational Databases - The data is stored in structured databases such as Oracle and SQL Server

3). Cloud Databases - The data is stored in one of the cloud platforms like Amazon Web Services and Microsoft Azure SQL

4). Open Database Connectivity (OBDC) - The ODBC allows access to the data that has been stored in formats that do not fall in the above three categories. Thus, OBDC is a standard applications programming interface that can be used to connect to various database management systems

Fig 1. Various data sources that can be explored using Tableau [1]

Data Field - Whenever a dataset is imported into Tableau it may have one or more tables. Each table in turn may have one or more columns or data fields that can be have different data types. In the Tableau each data field is specified as either a dimension or a measure (discussed later).

Fig 2. Data Fields in Tableau [2]

Data Types - Data type is used to constrain the set of possible values that a variable or a data item can take and the kind of programming operations that can be performed on it. It is used to show whether the particular data field is a number or boolean or a string. Each of the data fields in the data pane has an icon on top that shows the data type stored in that field. The different types of data included in Tableau are as follows:

1). Text or String

2). Data and Time Value

3). Numerical Value

4). Boolean Value

5). Geographical Value

6). Cluster Groups

Fig 3. Data Types in Tableau with the Icons [3]

Dimensions and Measures - In Tableau the data fields are classifies as either measure or dimension. A dimension is the field that contains non-numeric data or data that cannot be used to compute a mathematical expression. Thus, the data like month, name, state, and product category are examples of dimensions. Usually the dimensions are string data type. In Tableau the dimensions are represented as blue pills in the view pane.

In contrast, measure are numerical type data fields. These measures contain data like age, value, cost, profit, and blood pressure. The measures can be used in mathematical expression to obtain new numbers that are measures and may give another piece of information. In Tableau the measures are represented as green pills in the view pane.

Fig 4. Dimensions and Measures are shown as blue and green pills respectively [4]

Continuous and Discrete Data - The data in Tableau is classified as either being continuous or discrete based on its mathematical interpretation. A continuous data is whole or uninterrupted and is numeric in nature. As such a continuous data can be added or averaged or aggregated. The examples include cost, profit, time, etc. The continuous data is represented as a green pill in Tableau.

In contrast, discrete values are individualized, separate and unique. The data can only take values from a limited set. It is not possible to add or averaged or aggregate discrete data. The examples of discrete data includes number of people, shoe size, house numbers, etc. The discrete data is represented as a blue pill in Tableau.

Filter - The filter in Tableau enables the user to choose what and how much data they want to see in a visual. The filters can be based on the dimensions, measures, or values. It allows in streamlining the data for analysis and visualization based on the problem being addressed. Since Tableau is used to analyze large volumes of data, the filter function helps in reducing the dimensionality of data and also improve the performance of analysis.

Aggregation - It is kind of function in Tableau that allows the multiple values in rows to be grouped together as input to form a single value of more significant meaning.

Workbook and Worksheets - A worksheet in Tableau is just like a sheet in Excel where individual analysis or figures can be made while a workbook is the main Tableau file which contains a collection of sheets. The worksheet may also contain dashboards and stories while the worksheet only have single views and are used to construct dashboards and stories.

Dashboard - A dashboard is a collection of several data visuals displayed simultaneously. This allows a side-by-side comparison of data on a single board. It alleviates the problem of shuffling between separate worksheets to look at different visuals of the data while performing a holistic analysis.

Story - A story is a sequence of visualizations put together in a manner that a set of information is conveyed about the data. It can be used to narrate insights and inferences drawn from the data in a more comprehensive and sequential manner.


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