Tableau is a Business Intelligence tool that helps organizations analyze their accumulated data and derive insights from it to aid business growth. Tableau is available as a desktop installation or as an online service. It is known for its ability to create intuitive visualizations over large volumes of data. Tableau Analytics provides a suite of applications to help businesses make the best use of its analytical features.

Tableau Prep Builder helps one to connect to data sources and prepare them for analysis. Once analyzed, Table Prep Conductor can be used to schedule and monitor jobs that create reports and dashboards. Tableau’s popularity comes from the ease of exploiting advanced analytics algorithms even for people without much background in analytics. This post is about the advanced analytical features of Tableau and how one can get started with them. It explores all the components provided by Tableau Analytics in great detail over the course of this article.

What is Tableau?

Tableau is one of the fastest-growing Data Visualization tools stamping its authority in the Business Intelligence industry. Tableau plays a key role in the simplification of the data analysis process through easily digestible visuals without any need for programming skills to operate it.

Tableau allows the non-technical users to create personalized dashboards to drill down into the data to extract actionable insights from it. This allows the companies leveraging Tableau to make better decisions to steer business growth. Tableau comes in handy for various use cases like:

  • Data Collaboration
  • Data Blending
  • Data Visualization
  • Business Intelligence
  • Query Translation to Visualization
  • Creation of No-code Data Queries
  • Managing Metadata

What is Tableau Analytics?

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The main focus of Analytics at Tableau is utilizing visual representation to drive the process of analytics. Tableau believes that observable feedback that can be analyzed allows you to better learn from it and guide the exploration better. Tableau Analytics employs a visual approach to ensure that answering questions continues to be intuitive.

Tableau leverages the human ability to spot visual patterns quickly through VizQL, which translates drag-and-drop actions to data queries. With Tableau Analytics, you are provided a flexible front-end for Data Exploration. Here are a few ways that Tableau Analytics is helping ease your workload:

  • Suggested Table Joins help simplify the Data Preparation process.
  • Instant Geocoding allows you to create maps with just a click.
  • The ‘Show Me’ option suggests the best visualizations for your data.
  • Automatic data role assignments help you explore at a faster pace.

Why Data Visualization is Important?

Data Visualization can help detect Null values of data items in large datasets by representing them distinctively. This helps professionals reduce the burden of finding these errors before working with the data. The visual representation of data helps both technical and non-technical professionals/personnel have an overview of what the data is about. They can then tinker and also draw conclusions based on what they see. Data Scientists build Machine Learning Models for Predictive Analysis. These models are trained with large datasets to improve them. When training the models, the results are evaluated with Data Science Visualization Tools to know how well the model is doing and where it is lacking. 

Data Scientists and Data Analysts, at times, work with real-time data to derive meaningful trends. As real-time data is always fluctuating, it becomes difficult to analyze it. This is where the data can be visualized using charts and graphs for better understanding. This helps in making informed decisions not just in Data Science but in Business Intelligence in general. The result of analysis at any point of processing can always be visualized. The visualization can be done by anyone with knowledge of Data Science Visualization Tools, not just a Data Scientist. So far the data is from a supported data source, a Data Science Visualization Tool can represent it in its supported formats such as Graphs, Curves, or Charts.

Why should my business use Tableau?

Businesses gather large amounts of data which includes both structured and unstructured data. In this context, Analytics helps in processing these data and provides actionable business insights. Using these data, the future trend can be predicted. Tableau makes this analytics more accessible without the help of experienced analytics professionals. Smaller businesses can easily adapt this and improve their business with Tableau Public at free of cost.

Are You Fully Utilizing Your Data?

Forbes article has stated that More data has been created in the past two years than in the entire previous history of the human race. It has been estimated that the amount of data in the world is now doubling every two years. But not all organizations are able to take advantage of this data in gaining valuable insights. One study found that, for Fortune 1000 companies, increasing access to data by 10% would result in an additional $65 million in net income. In addition, retailers who take full advantage of their big data could increase operating margins by up to 60%.

Analyze Data and Get Insights Faster with Tableau


  • Tableau Desktop or Tableau Online. 
  • Basic understanding of Data Analytics.

What is Advanced Tableau Analytics?

Tableau allows you to accomplish so much by collating the wisdom of your company’s top experts by leveraging collective insights. Tableau understands that an analytics pipeline should be created in a fairly short span of time and should be faster to market. To make the most of your investments to deliver the right solution to your customers, you can incorporate various advanced Tableau Analytics features. Tableau’s advanced analytics feature set broadly contains the below features:

You will now learn in detail about each of these features and how to use them in Tableau in the following sections.

Tableau Analytics: Data Segmentation and Analysis

Segmenting data is about grouping data based on common characteristics. When the data being segmented is user data and the common characteristics are behavioral features, it is called Cohort Analysis.

Cohorts are nothing but groups of users exhibiting the same behavioral patterns. An example of Cohort Analysis could be a question like this ‘Do customers who pay using credit cards order more items’. All the customers that pay using credit cards become a Cohort and the analyst then calculates the average purchase value of such customers and compares it with the overall Average Purchase Value. 

Tableau makes it easy to do such analysis with just a few drag and drop of the relevant columns. For example, the following Tableau Dashboard shows the behavioral patterns of patients in a clinic. It groups the number of patients by their Time of Arrival and shows the Average Wait Time experienced by each user.

Another aspect of segmenting customers automatically is Clustering. Clustered customers are nothing but a group of customers that belong to the same patterns. Clustering differs from Segmentation in the sense that it deals with finding similar groups based on mathematical algorithms rather than forming segments first and then grouping data based on that. Creating clusters in Tableau is as simple as dragging the cluster model found in the analytics tab to the analysis area. 

Tableau will automatically generate clusters based on fields. The analyst has the option of changing the number of clusters or adding more fields if he is adventurous enough.

Tableau Analytics: What-if and Scenario Analysis

What-if Analysis helps one to explore different scenarios after he has created a dashboard or an analysis report. Tableau achieves this by allowing analysts to define parameters while building their analysis, These parameters can be altered later even after the dashboard is built and the user can see how his results change when the parameters are changed.

For example, let’s say a senior manager is trying to build a Sales Commission Model trying to balance his Revenue Growth and Salesman Satisfaction. He can define the base salary of the salesman as a parameter while building his model and later try different values for this parameter and see how the end result looks like. 

Creating parameters in Tableau is as easy as clicking the ‘Create Parameter’ link in the Dimension section in the Data pane.

Tableau then opens up a dialogue box that allows the users to select the data type and define possible values for the parameter.

These parameters can then be used in Calculated Fields to create analysis results. Next up is the concept of Calculated Fields.

Tableau Analytics: Advanced Calculated Fields

Calculated Fields are fields that were not present in actual data but created by transforming the actual fields. For example, if your user data only contains the Time of Login, but you need a discreet data field indicating whether the login was on forenoon or afternoon, that can be a Calculated Field. Tableau allows users to create Calculated Fields based on arithmetic operations, conditional expressions, etc.

It is accomplished through Tableau’s proprietary expression language called the Level of Detail Expressions. The Level of Detail Expressions makes it easy to represent complex aggregations. They are extremely valuable in the Cohort Analysis. You can get to Calculated Fields by clicking on ‘Create Calculated Field’ in the dimension drop down in the Data pane.

A detailed write-up on how to use Level of Detail Expressions for Cohort Analysis is available here

Another important feature that deserves a mention here is the Table Calculations, They allow users to apply logic to all columns or rows in a table and derive Calculated Fields.

Tableau Analytics: Time-Series Analysis and Forecasting

Tableau’s Time Series Analysis helps users to visualize how the variables are changing with respect to time. Tableau’s ability to intuitively filter date and time ranges helps one to dig deep into these variations and derive insights. For example, given below is a dashboard that displays profits and discounts against the month. 

Forecasting is Tableau is done by using Trends or Smoothing operations. A Trend Line tries to fit a line in a scatter plot representing the general trend of variables. Creating a Trend Line in Tableau is as easy as dragging and dropping the ‘Trend line’ found in the Analytics tab to your scatter plot.

An expert user with a background in analytics can click on the generated line and understand the equations and constants that generated the line. 

Forecast based on Exponential Smoothing is Tableau’s default Forecast Mechanism. A Smoothing operation tries to find a weighted average of the changing values, thereby eliminating any randomness and focussing only on the general direction of the variable. This can be accessed by dragging and dropping the forecast icon found in the analytics tab as above. As usual, an adventurous analyst with knowledge can exploit Tableau’s Forecasting functionality even better by trying to change the Length of the Forecast or Forecasting Model itself. 

Tableau Analytics: Accessible data analytics

With tableau you can access the proactive data from slack. Tableau provides a scope to raise natural language questions via Slack. To raise questions in natural language and receive digital reply is an advantage. Tableau Slack integration shows the company is taking a similar approach to Microsoft Power BI, which offers users a Power BI tab in Microsoft Teams, but goes a step further to offer in-app natural language interaction for users, so they can ask questions rather than passively consuming analytics content.

Tableau Analytics: Analyze Data and Get Insights Faster

In the recent times, static data tables and charts does not provide the required insights. Visual analytics is needed to more quickly gain insights in a simple and meaningful way. Transforming your data into a visual story, makes it more intuitive, engaging, and useful.

Data visualizations created with Tableau enables you to answer probing and important business questions such as:

  • Who is our audience?
  • What are they interested in?
  • What marketing efforts are or aren’t working?
  • Where is our growth coming from?
  • Where are our sales declining? Why?
  • What is our product mix?
  • How are new products performing?


The biggest advantage of using Tableau for Advanced Analytics is that it is so intuitive that even a user without much background in analytics can run advanced algorithms with a few clicks. Beyond the methods mentioned here, Tableau also supports integration to external tools like Matlab and languages like Python and R. The slight hiccup in using Tableau is the lack of native support when it comes to connecting data from cloud-based services like Hubspot. In case your organization uses a mix of on-premise and cloud-based data sources, using a completely managed cloud-based ETL tool like Hevo can solve this problem for you.

Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse to be visualized in a BI tool such as Tableau.


Hevo Data, a No-code Data Pipeline, helps you transfer data from a source of your choice in a fully automated and secure manner without having to write the code repeatedly. Hevo, with its strong integration with 100+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiff.

Want to take Hevo for a spin? 

SIGN UP and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

Software Developer, Hevo Data

Talha is a Software Developer with over eight years of experience in the field. He is currently driving advancements in data integration at Hevo Data, where he has been instrumental in shaping a cutting-edge data integration platform for the past four years. Prior to this, he spent 4 years at Flipkart, where he played a key role in projects related to their data integration capabilities. Talha loves to explain complex information related to data engineering to his peers through writing. He has written many blogs related to data integration, data management aspects, and key challenges data practitioners face.

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