Most companies today rely on data-driven decision-making to drive business growth. Data-driven decision-making can be defined as the process of making organizational decisions based on actual data rather than intuition or observation alone.

  • This has led to an exponential increase in the use of various Data Analytics and Business Intelligence techniques in most companies.
  • There are a wide variety of tools available in the market that are leveraged by businesses to perform an in-depth analysis of their data in order to plan future Growth, Product, and Marketing strategies accordingly. One of the most well-known Business Intelligence tools in Tableau.  

Advanced Analytics in Tableau

The following scenarios can easily be handled using Advanced Analytics in Tableau:

1. Segmentation and Cohort Analytics in Tableau

Tableau provides flexible tools for intuitive and rapid Cohort Analysis & Segmentation. It can seamlessly perform slicing and dicing operations on data along as many dimensions as required. Automated Clustering can also help in this Segmentation using the pre-processed datasets. 

Features for Segmentation and Cohort Analytics in Tableau

A) Clustering

Clustering is an Unsupervised Machine Learning technique. Tableau provides users the ability to use this technique for segmenting data, particularly if a large number of variables are available. 

Tableau’s interactive and flexible interface gives users the ability to test different hypotheses and explore distributions present among different Cohorts. 

Analytics in Tableau: Clustering | Hevo Data
B) Sets & Sets Actions

Sets are used to define collections of data objects either by manual selection or by using an automated logic. Sets can be useful in various scenarios including Filtering, Highlighting, Cohort Calculations, Outlier Analysis, etc.

There is also the option of combining multiple Sets in order to test different scenarios or create multiple Cohorts for simulations like Customer Groups for Retention Analysis Patients Groups for Health Control or Non-health Control Groups. 

Analytics in Tableau: Sets & Sets Actions | Hevo Data
C) Grouping

Grouping is a feature for creating Ad-hoc Categories and establishing hierarchies. Groups can also help with basic Data Cleaning operations.

Groups can be useful when data is not consistent by providing consistency and quality issues.

Analytics in Tableau: Grouping | Hevo Data

2. Scenarios and What-If Analytics in Tableau

Tableau offers a very flexible User Interface that can be used with powerful input capabilities that are extremely helpful in making calculations and testing different scenarios. So by just using the interface and changing the parameters, its effect on the output can be seen. This feature is useful for quick Data Analysis and to validate different results. 

Features for Scenarios and What-If Analytics in Tableau

A) Parameters

Parameters help in changing the base value of calculations or changing the initial conditions. These help in making calculations, setting up Filter Thresholds, and giving users the ability to choose what they wish to see on their dashboards. This feature also helps non-technical users in understanding the data & view the Data Sections they are interested in.

Analytics in Tableau: UTM Parameters | Hevo Data
B) Story Points

As the name suggests, Story Points allows users to define their scenarios and figure out if these scenarios can be validated by the data or not. All the above-mentioned features including Sets, Groups, Parameters, and Drag-and-Drop Segmentation help in the creation of Useful and Visual Stories that anyone in the hierarchy with a technical or non-technical background can understand.

Overall, the What-If Analytics in Tableau can help in performing more complex Data Analysis along with greater visualization effects making it easy for users to understand the data and hence improve the overall decision-making of the organization. 

3. Sophisticated Calculations and Statistical Functions

Tableau has many sophisticated and detailed functionalities including the implementation of Statistical Methodologies like Skewness, Correlation, Covariance, Kurtosis, Mode, Standard Deviation, etc. along with Statistical Models like Naive Bayes, K-Means, Random Forest, etc. All these techniques help in better understanding of data and making predictions seamlessly when required.

Features of Sophisticated Calculations and Statistical Functions

A) Calculated Fields

The calculated Field primarily helps in solving complex computations. These fields allow users to generate new data points from the data that already exists. The advantage of these fields is that it allows users to perform various arithmetic operations and complex logic. 

The two types of calculated fields that help in devising this Advanced Analytics in Tableau are as follows:

  • Level of Detailed Expression (LOD)

These expressions are of paramount importance in performing complex analyses of the data. They are a part of Tableau Calculation Language. This type of complex analysis was not possible in earlier versions of Tableau until LOD was introduced in Tableau 9. 

  • Table Calculations

Tableau Table Calculations can be performed on the part of data that is currently in the visualization. These calculations are applied to many values that are present in the table and are dependent on the table structure as well. In Tableau, there are two ways to work with Table Calculations i.e. Quick Table Calculations and Table Calculation Functions.

Quick Table Calculations lets you define a Table Calculation with just one click. You can read more about it in the Official Documentation.

Table Calculation Functions are expressible in the same calculation language. You can use one of the Quick Table Calculations as a starting point and edit it manually if you need additional complexity. More Details can be found on the Official Documentation. 

Analytics in Tableau: Table Calculations | Hevo Data

4. Time-Series and Predictive Analytics in Tableau

Time Series is one of the most important types of analysis that can be performed on almost every data set to explore the trends and seasonality including Predictive Analysis and Forecasting. 

Features of Time-Series and Predictive Analytics in Tableau

Tableau provides a very interactive and user-friendly interface to perform Time-Series Analytics in Tableau. This analysis begins by dragging the fields of interest into the view and beginning the questionnaire process. With the help of the dual-axis feature and Discretized Aggregation, one can look at the multiple time series and perform its analysis easily.

Analytics in Tableau: Time-Series Analysis | Hevo Data

Forecasting is another important feature of Time-Series Analytics in Tableau. By simply using the Drag-and-Drop feature, this analysis can be performed in a few clicks. 

Analytics in Tableau : Forecasting | Hevo Data

5. R and Python Integration

R and Python integrations provide the power and ease of use of Tableau while allowing experts to leverage prior work in other platforms and handle nuanced Statistical and Machine Learning requirements.

Features of R and Python Integrations in Tableau

Tableau is a Comprehensive Analytics platform that houses the ability to integrate with other Advanced Analytics technologies, allowing you to expand the possible functionality and leverage existing investments in other solutions. For further details, you can have a look at the Official Documentation.

What is Advanced Analytics?

Advanced Analytics can be defined as an Autonomous or Semi-Autonomous examination of data or content using sophisticated techniques or tools. Autonomous and Semi-Autonomous means how much useful information is being extracted out of data and is being used to understand the trends and behaviors that the data represents. This information is extracted using various Machine Learning and Data Science tools and techniques like Predictive Analytics, Recommendation Systems, Statistical Forecasting, etc. to identify deeper insights from the data. 

Advanced Analytics also includes pre-processing the data to understand the actual problem statement. It is often helpful to analyze feature separability using a Multivariate Visualisation technique. It may require devising a Classifier or Regressor using an appropriate Machine Learning method for a comprehensive evaluation of the finally chosen Data Pre-processing and Analysis Pipeline.

What is Tableau?

Tableau is a popular Data Analysis & Visualization tool that was built in 2003. It started out as a Computer Science project at Stanford University that aimed to improve the flow of analysis and make data more accessible to people through interactive visualization techniques. 

Key Features of Tableau

Some of the key features of Tableau are as follows:

  • Advanced Dashboard
  • In-Memory and Live Data
  • Attractive Visualizations
  • Robust Security
  • Predictive Analytics


  • This article gave you an in-depth understanding of the various types of Advanced Analytics in Tableau.
  • These Advanced Analytics features make Tableau one of the best tools available for Data Analytics & Visualization allowing organizations to make smart decisions.
  • Most modern businesses use multiple platforms to run their day-to-day operations. In order to perform any analysis on this operational data, the data would first have to be integrated from all these platforms and stored in a centralized location.
  • Making an in-house Data Integration platform would require immense engineering bandwidth. Businesses can instead choose to use Automated Data Integration like Hevo
Muhammad Faraz
Technical Content Writer, Hevo Data

Muhammad Faraz is an AI/ML and MLOps expert with extensive experience in cloud platforms and new technologies. With a Master's degree in Data Science, he excels in data science, machine learning, DevOps, and tech management. As an AI/ML and tech project manager, he leads projects in machine learning and IoT, contributing extensively researched technical content to solve complex problems.

No-code Data Pipeline For Tableau