Data Blending in Tableau: A Comprehensive Guide For 2021

Introduction

The evolution of data has made it an attractive source to power businesses. Categorizing and combining data has assisted companies with driving maximum results, but this given task was complex and impossible due to the vast number of differences in every data source. But today, Business Analytics tools help not just with combining data but also with giving optimal results from the data. One such tool used for compiling multiple sources of data under one sheet is Tableau Data Blending. Data Blending in Tableau is in high demand among the various enterprises and to be able to capitalize further on this, Tableau is expanding its offerings to Tableau viewers, creators, and explorers. 

So, what is Data Blending in Tableau? Data Blending is an important feature in Tableau that lets multiple data be assessed using similar dimensions with one single view. This leads to maximum computational efficiency. Let us dig a little deeper into the meaning behind Data Blending in Tableau and more.

1. About Tableau
2. What is data blending in Tableau
3. Why Do You Need Tableau In Data Blending?
4. When is Data Blending used?
5. How does data blending work in Tableau
6. How is Data Blending different from Data Joining
7. When to substitute joining for blending
8. Types and Sources
9. Limitations
10. Filtering with Data Blend

1. About Tableau

According to reports by Entrepreneur India, by the year 2023, the data industry will be valued at a whopping  $77 billion. Data Analytics has become a vital part of business growth and hence tools that support analyzing data are in high demand. One such popular analytical platform is Tableau. It is a business intelligence platform that transforms interactive data for meaningful insights. This is a powerful and rapidly growing platform used for the simplification of raw data. This tool has garnered high demand amongst businesses that are keen on using data to their advantage. Tableau is commonly used for its feature in Data Blending. Let us take a deeper look into what data blending actually is!

2. What Is Data Blending In Tableau?

Data Blending in Tableau is a crucial feature of this platform that is used to analyze the data that gives one single view among the multiple sources of data. Data blending simplifies large portions of data to receive customized results, and this is what gets the company optimal data-driven results. Data blending has an important role to play in any data cycle of an organization and this makes it vital.

Data blending combines the data from various distinct sources and then brings in additional data from another secondary source. It then displays this data in the primary source in a single view. Data blending also combines the supplement of the table from one of the data sources with the column from another data source. They are mostly used to combine the data and at times these get backed by factors like granularity and data type. So, now that we know what Data Blending is in Tableau, let us learn more about its uses.

3. Why Do You Need Tableau In Data Blending?

Tableau Data Blending gives the user an option to combine and join the various sources of data. The process of joining and mixing is different in the tableau. The Data Blending in Tableau allows combining the data sources so that the data can be integrated rightfully. Tableau Data Blending is less fussy while compiling data on a single worksheet. Here the data that is accumulated from various sources goes on to get analyzed by a single view.

Through multiple sources, raw data is procured without any filtering or assessment. So by using data blending the data gets refined and assimilated according to the customer needs. Another instance where data blending is used is when you want to combine data from different joins. With the help of Data Blending in Tableau, you can combine this on a single sheet. 

4. When Is Data Blending Used?

Here is how the data blending comes into use by the tableau developer. Suppose you have the data stored in two different databases.

• When the data granularity that is captured in each of the tables is different in the two sources of data then data blending comes in handy as the best way to combine the data. It also ensures granularity is not increased.
• Data blending is used when you want to combine the data from various sources that are not supported by the cross-database joints. In this case, you will have to set up a different data source for the data that you wish to analyze. You then make use of the data blending to use a single sheet to combine the source of data.
• Data blending is also used when the data is at different detail levels. At times one data set will capture the data using various detail levels. This could be that one has lesser or greater granularity. Data blending helps to combine this data.

5. How Does Data Blending Work In Tableau?

Data blending in Tableau uses distinct sources to analyze data with the help of standard dimensions. At least two data sources must be connected. Using the sources a common dimension is created. Here queries are sent to each data source in the database. The results of all queries from the sources are sent to Tableau as aggregate data that is combined together.

This occurs sheet-by-sheet and the order in which the given data fields are used will determine the order of data sources (primary and secondary). This process works when data in the primary source is supplemented for data in the secondary based on the field it is linked to. When Tableau identifies one value that matches in both sources a relationship is formed and data is blended based on that. But if it fails to show a common linking field, the value ‘null’ appears on the sheet.

6. How Is Data Blending Different From Data Joining?

Although these two methods aim for one purpose, that is combining data for analysis, their approaches aren’t the same. As we have already discussed before, data blending uses multiple sources to derive efficient data-based results whereas Data Joining uses only one single source for data. Data Joining usually cannot be mechanized when the data set is large. This is used outside Tableau whereas data blending is used within Tableau.

Another reason for Data Joining to be replaced with data blending is when certain databases fail by the functioning of Data Joining. In Data Joining, data has to be of one level of granularity but in Data Blending it can accommodate various levels of granularity. Another function Data Joining fails to perform, that blending does is that it separates each data set and sends queries to them before blending.

7. When To Substitute Joining For Blending

• Duplicate data – When Data Joining is used, there are high chances for the data to duplicate itself. Hence data blending is used to avoid further duplication.
• Data Cleansing – Sometimes the data don’t match up by using the joining method. Hence to create a clear dimension and to compile the data blending is used.
• Large proportions of data – When joining is used for large sets of data, it tends to lack efficiency and can lead to faulty errors. Whereas by applying the blending method large amounts of data can be combined with no hassle.

8. Types And Sources

Tableau Data Blending assesses a common ground on which data is filtered. This commonly known as relationship and is of two types

• Automatically Defined Relationship – This form of relationship is best when the field in which the analysis works consists of simple data from both data sources. This relationship is picked automatically by the platform after analyzing the data.
• Manual Tableau Data Blending – This is a process that is used when there is a scenario that needs complex filtering. This occurs when the data from the sources have less in common. Like when there is a budget comparison data from spreadsheets with complex numerical the relationship can be chosen manually.

Apart from types, the data that is retrieved for blending is from two key sources-

• Primary source – This source is the key source for data. This is the main database that is used by Tableau. 
• Secondary source – There needs to be at least one secondary data table for data blending to take place. But only the data that is corresponding to the data from the primary source is taken from the secondary source. Hence this source is largely dependent on the former. 

Like in the case of a sales and a target business the operation needs to be performed in the Tableau Data of the two different data sources. Here you blend the primary and the secondary data of sales. The two data sources should have one common file that is derived from the primary source. When the secondary data source is switched in the window then tableau links the first name automatically. A custom relationship gets formed to create an exact mapping between the two fields.

9. Limitations

Here are the tableau data blending limitations:

• While combining large amounts of data some information might get missed out.
• Blended data cannot be published as a unit so the data must be published separately.
• Tableau Data blending compromises on the speed of query in high granularity
• When the data is calculated it does not get listed in the sorted fields drop-down dialog box.
• The cube data sources can only function as a primary source to blend the data in Tableau and these cannot be used as a secondary source of data.
• It is difficult to use non-additive aggregates such as MEDIAN and COUNTD while blending data.

10. Filtering With Data Blend

While blending multiple data sources, to seek common ground to link these sources, filters are created. This filter can be applied across all sources. The filtering of data blending is done from the two key sources, the primary key source, and the secondary key source. Filtering from the primary source is commonly practiced as it helps derive common filters that will go on to show relevant values. Whereas the use of the secondary source for filtering is not popular as it has its drawbacks. Unless the two sources are linked it cannot give out relevant values on common ground. 

Conclusion

Data analysis has established itself as an important aspect of business growth. The growth and demand for this have accelerated over the last few years, hence the scope of business analytics is infinite. This simple tool for data analysis has helped bring optimal results for businesses. The scope of data blending is vast and has become a popular method to compile data to receive accurate results. Data Blending in Tableau helps you get rapid actionable results using a flexible and dynamic approach.

This method of blending data has become a capital-intensive demand for businesses.
The smooth flow of this business analytics model largely depends on not just the method but expert use.  Therefore choosing the right business analytics expert is key for analytical tools like Data Blending.

If you are interested in making it big in the world of data and evolve as a Future Leader, you should consider our Integrated Program in Business Analytics, a 10-month online instructor-led program, to become an IIM-Indore certified Business Analyst.

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