How to work with the Boolean data type in Google Data Studio
If a data field in your data set can have only one of the two possible values: true or false then you should use the Boolean data type while setting up your data source schema.
For example, consider the following data set:
Here the field ‘Is Revenue > Forecasted Revenue’ can have only two possible values: TRUE or FALSE.
For Google Sheets, the values TRUE and FALSE are of type boolean so when setting up the data source schema, we should set the data type of the field ‘Is Revenue > Forecasted Revenue’ to ‘Boolean’:
This seems pretty straightforward, doesn’t it? But here is the thing. This is not going to work.
When you create a report from this data source which uses the ‘Is Revenue > Forecasted Revenue’ field, you will get a system error:
That happened because Data Studio did not recognize the boolean values stored in Google Sheets as a boolean value.
Instead, it treats these boolean fields as ‘text’ fields:
No matter how you store boolean values in Google Sheets (whether as true/false, TRUE/FALSE, 0/1, yes/no), Data Studio won’t recognize them as boolean values. If you want to use a boolean field in Data Studio then you would need to create and use a calculated metric.
Get the ebook on Google Data Studio (50+ Pages)
Follow the steps below:
Step-1: Navigate to your data source editor and then click on the ‘ADD A FIELD’ button on the top right-hand side:
Step-2: Enter the name of the new calculated field. For example: ‘[NEW] Is Revenue > Forecasted Revenue’:
Step-3: Enter the following formula in the ‘Formula’ field:
Make sure you see the green-colored checkmark at the bottom of the formula box. This checkmark indicates that you have entered the formula correctly.
If the formula that you have entered is not correct then you will see this symbol 
instead of the green checkmark.
I entered the following formula: case when Is Revenue > Forecasted Revenue = “TRUE” then true else false end
This means; if the value of the field ‘Is Revenue > Forecasted Revenue’ is ‘TRUE’ then return true. Otherwise, return false.
The following formula won’t work:
In Data Studio, you can compare a dimension or metric only with a literal value.
Here we are comparing the ‘Revenue’ dimension with another dimension called ‘Forecasted Revenue’ via the logical expression: Revenue > Forecasted Revenue
Step-4: Click on the ‘Save’ button.
Step-5: Now click on ‘All Fields’ in order to navigate back to the data source editor:
You should now see the new calculated field, listed in the data source schema:
When you create a report based on this data source which uses the new calculated field, it would look like the one below:
Following is a short video which shows how I created the calculated field and then used it in my report:
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Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and Beyond
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
Himanshu Sharma
Digital Marketing Consultant and Founder of Optimizesmart.com
If a data field in your data set can have only one of the two possible values: true or false then you should use the Boolean data type while setting up your data source schema.
For example, consider the following data set:
Here the field ‘Is Revenue > Forecasted Revenue’ can have only two possible values: TRUE or FALSE.
For Google Sheets, the values TRUE and FALSE are of type boolean so when setting up the data source schema, we should set the data type of the field ‘Is Revenue > Forecasted Revenue’ to ‘Boolean’:
This seems pretty straightforward, doesn’t it? But here is the thing. This is not going to work.
When you create a report from this data source which uses the ‘Is Revenue > Forecasted Revenue’ field, you will get a system error:
That happened because Data Studio did not recognize the boolean values stored in Google Sheets as a boolean value.
Instead, it treats these boolean fields as ‘text’ fields:
No matter how you store boolean values in Google Sheets (whether as true/false, TRUE/FALSE, 0/1, yes/no), Data Studio won’t recognize them as boolean values. If you want to use a boolean field in Data Studio then you would need to create and use a calculated metric.
Get the ebook on Google Data Studio (50+ Pages)
Follow the steps below:
Step-1: Navigate to your data source editor and then click on the ‘ADD A FIELD’ button on the top right-hand side:
Step-2: Enter the name of the new calculated field. For example: ‘[NEW] Is Revenue > Forecasted Revenue’:
Step-3: Enter the following formula in the ‘Formula’ field:
Make sure you see the green-colored checkmark at the bottom of the formula box. This checkmark indicates that you have entered the formula correctly.
If the formula that you have entered is not correct then you will see this symbol 
instead of the green checkmark.
I entered the following formula: case when Is Revenue > Forecasted Revenue = “TRUE” then true else false end
This means; if the value of the field ‘Is Revenue > Forecasted Revenue’ is ‘TRUE’ then return true. Otherwise, return false.
The following formula won’t work:
In Data Studio, you can compare a dimension or metric only with a literal value.
Here we are comparing the ‘Revenue’ dimension with another dimension called ‘Forecasted Revenue’ via the logical expression: Revenue > Forecasted Revenue
Step-4: Click on the ‘Save’ button.
Step-5: Now click on ‘All Fields’ in order to navigate back to the data source editor:
You should now see the new calculated field, listed in the data source schema:
When you create a report based on this data source which uses the new calculated field, it would look like the one below:
Following is a short video which shows how I created the calculated field and then used it in my report:
Register for the FREE TRAINING...
"How to use Digital Analytics to generate floods of new Sales and Customers without spending years figuring everything out on your own."
Here’s what we’re going to cover in this training…
#1 Why digital analytics is the key to online business success.
#2 The number 1 reason why most marketers are not able to scale their advertising and maximize sales.
#3 Why Google and Facebook ads don’t work for most businesses & how to make them work.
#4 Why you won’t get any competitive advantage in the marketplace just by knowing Google Analytics.
#5 The number 1 reason why conversion optimization is not working for your business.
#6 How to advertise on any marketing platform for FREE with an unlimited budget.
#7 How to learn and master digital analytics and conversion optimization in record time.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and Beyond
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
Himanshu Sharma
Digital Marketing Consultant and Founder of Optimizesmart.com
