Data Blending in Tableau

In this article, we will discuss data blending in tableau, steps to create, benefits and limitations and finally the difference between joins and blend in tableau.

Table of Content

• What is Data Blending
• Steps for Blending Data
• Key Points related to Data Blending
• Benefits of Data Blending
• Limitation of Data Blending
• Difference between Joins and Data Blending

In today’s world data is increasing very rapidly, and according to a report published by statista.com, the global data creation is projected to grow to more than 180 zettabytes ( 1 zettabyte = 1 billion terabytes) and these data are stored in different data sources like Oracle database, SQL server, google analytics or in different excel workbooks.

Now, the question arises of how to analyze the data that are stored in different data sources.

Tableau offers different ways to combine data in tableau:

• Relationship
• Join 
• Blend

Now let’s explore, what data blending is?

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What is Data Blending?

Data Blending is one of the powerful features of tableau that combines data from different (multiple) data sources and displays the data of secondary data source and primary data source together directly in the view for easy and quick analysis.

Confused, with the definition, so let’s understand by a situation:

Let you want to open a restaurant, so before opening the restaurant you want to analyze the data of different cuisines served around the world and the number of sales of these cuisines in your city.

But when you start analyzing the data in tableau you find that cuisines data are stored in an excel workbook and the sales data is stored in SQL.

Now how you will analyze the data, here data blending comes in handy that will help you to combine your data.

Now, we will take an example to understand how to create data blending in tableau.

Steps for Blending Data

Problem Statement:  

Here we have two datasets, Sales data of H&M for the years 2018 and 2019. The only difference between both datasets is the name of one field.

In the 2018 dataset, the field containing Central, East, West, South, and North is referred to as Zone while in the 2019 dataset it is referred to as Region

Steps to Create

• Connect H&M Sales 2018
• Connect H&M Sales 2019 using data options from the toolbar
  • Select Data (from Toolbar) -> Select New Data Source -> Select H&M Sales 2019
• Drag and Drop Sales and State into Column and Row shelf respectively from H&M sales 2019
  • Drag and Drop Zone into color in Marks Pane
• Drag and drop Sales into column shelf from H&M sales 2018

• Select Data -> Select Edit Bend Relationship
  • Blend Relationship dialog box will appear

• It contains both Primary and Secondary Data Source
• It also contains a list of the automatically detected relationships between the field of both the year of sales data
• If you want to make a new relationship between the fields of both the dataset, you can choose the custom option

As the fields Zone and Region both contain the same entries, so we will create a relationship between both using custom

• Select Custom -> Select Add
  • Add/Edit Field mapping dialog box will appear

• Select Zone from Primary Data Source 
• Select Region from Secondary Data Source
• Click Ok
• Click OK (Blend Relationship Dialog Box)

Note: This relationship is applied only to the current workbook as blend work on a sheet-by-sheet basis.

Now, we can use both datasets for our analysis.

• In the data pane, you will find the link icon in front of the fields
  • These links indicate that you can use the data from the primary data source as a common field

From the above screenshot, we will get the total sales in the year 2018 and 2019.

The important thing is that we can visualize the data region and state-wise both in a single graph

Key Points related to Data Blending

• It behaves like a left join, which may lead to missing data from the secondary data source
  • Left Join only shows the common entries of both the dataset
• The primary data source is indicated with a blue check while the secondary data source has an orange check mark
  • Primary and secondary data sources are determined by the order in which the fields are used.
• It occurs on a sheet-by-sheet basis 

Benefits of Data Blending

• It is sufficient to satisfy the need for Exploratory Visual Analytics
• Provides an accurate aggregate of data from multiple sources
• It is best while using the large dataset as it combines the data before and then aggregates it for the view which increases the performance
• Data blending is useful when the field values exist at different levels of detail (have different granularity)

Limitations of Data Blending

• If the secondary data source has LOD (have different granularity), they are taken down after data blending
• Data from secondary data sources must be aggregated
• Non-additive aggregates like SUM, COUNT, MEAN, and MEDIAN have data blending issues

Difference between Joins and Data Blending

Join

• In join, we can combine the data from the same source only
• Join combine the data and then aggregate

Left Join

A left join returns all rows from the left table and any corresponding rows from the right table. 

Blend

• In blend, we can combine the data from multiple sources
• Blend aggregates the data and then combine

Note: (*) represents multiple values.

Conclusion

In this article, we have discussed data blending in tableau, steps to create data blending, benefits and limitations and finally the difference between joins and blend in tableau.

Hope this article will help you in your data analysis journey.

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