Guide to Data Types in Google Data Studio
The data type of a data source field determines the kind of data to expect (in the connected data set) when processing the field.
For example, when the data type of a field is ‘Number’, it tells Data Studio to expect a number when processing the field:
When the data type of a field is ‘Currency (US – Dollars) ’, it tells Data Studio to expect currency data in US dollars when processing the field:
Similarly, when the data type of a field is ‘Text’, it tells Data Studio to expect text data when processing the field:
The data type determines which operations are allowed and not allowed on a data source field.
Get the ebook on Google Data Studio (50+ Pages)
For example, you can’t apply an arithmetic function to a ‘Text’ field or use a ‘Number’ field as the date range dimension in a report. If you want to change the data type of a field then just click on the drop-down menu next to a data type.
Note: Changing the field type can have a considerable impact on how you see your data in reports.
Google Data Studio supports the following data types:
- Numeric
- Text
- Date and Time
- Boolean
- Geo
- Currency
- URL
Numeric data types
There are three numeric data types in Google Data Studio:
- Number – select this data type if you want Data Studio to expect a number (includes floating-point number) when processing the field in the specified data set.
- Percent – select this data type if you want Data Studio to expect percentage data when processing the field in the specified data set.
- Duration – select this data type if you want Data Studio to expect time duration in seconds when processing the field in the specified data set.
For example, consider the following data set:
The field ‘Number of orders’ is of type ‘number’.
The field ‘Percentage of Sales’ is of type ‘percent’.
The field ‘Duration’ is of type ‘duration’.
If we connect this data set to our data source schema then when deciding the data source schema (structure) we should:
- set the data type of the field ‘Number of orders’ to ‘Numbers’.
- set the data type of the field ‘Percentage of Sales’ to ‘Percent’.
- set the data type of the field ‘Phone Call Duration’ to ‘Duration’.
To learn more about numeric data types in Google Data Studio read the following two articles:
#1 Google Data Studio Number Formats / Data Types
#2 Doing Basic Maths on Numeric Fields via Calculated Fields
Text data type
Select the ‘Text’ data type if you want Data Studio to expect text when processing a field in the specified data set. A text data type can include any combination of letters, numbers, special symbols (like [, }, @….) and other characters.
Consider the following data set:
Here all the values of the field ‘Customers Name’ are of type ‘Text’. So when defining the data source schema, we would set the data type of the field ‘Customers Name’ to ‘Text’:
To learn more about the text data types, read this article: How to Work with ‘Text’ Data Type in Google Data Studio
Date and time data types
As you can see from the screenshot, Google Data Studio supports several different types of date and time.
The data types for ‘date’ fields can be divided into the following two categories:
- Absolute Dates
- Relative Dates
The data types for ‘time’ fields can be divided into the following two categories:
- Absolute time
- Relative time
Absolute date
An absolute date refers to a specific date that you can point to on a calendar. Following are examples of absolute dates in the context of Google Data Studio:
- 2019
- Q1 2019
- Nov 2019
- Nov 10, 2019
Absolute date types
These are the data types meant for absolute dates. Following are absolute date types:
You should select that data type for your absolute date which matches the date type in your data set.
Absolute time
An absolute time refers to a specific time that you can point to on a clock.
Following are examples of absolute time in the context of Google Data Studio:
- Sept 10, 2019, 10 PM
- Sept 10, 2019, 10:30 PM
Data types for absolute time
These are the data types meant for absolute times. Following are data types for absolute time:
You should select that data type for your absolute time that matches the time type in your data set.
Relative date
The relative date is the date that does not refer to a specific date.
Following are examples of relative dates in the context of Google Data Studio:
- Q1, Q2, Q3, Q4 – here we don’t know, we are referring to the quarter of which year.
- Jan, Feb, March, April… – here we don’t know, we are referring to the month of which year.
- Mon, Tue, Wed, Thu… – here we don’t know, we are referring to the day of which week or month or year.
Relative date types
These are the data types meant for relative dates. Following are relative date types:
You should select that data type for your relative date which matches the date type in your data set.
Relative time
Relative time is the time which does not refer to a specific point in time.
Following are examples of relative time in the context of Google Data Studio:
- 1 am, 3 am, 7 pm, etc – here we don’t know which day, month or year the time occurred.
Data types for relative time
These are the data types meant for relative times. Following are data types for relative time:
You should select that data type for your relative time which matches the time type in your data set.
To learn more about the date and time data types in Data Studio read this article: Tutorial on Date and Time Data Types in Google Data Studio
Boolean data type
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.
To learn more about working with the boolean data type, read this article: How to work with Boolean data type in Google Data Studio
Geo data types
Use ‘Geo’ data type if you want Data Studio to expect a geographic region (like a city, region, country, continent) when processing a field in the specified data set.
Following are the various Geo data types available in Google Data Studio:
To learn more about the geo data types, read this article: Geo Data – Country, Region, Latitude, Longitude in Google Data Studio
URL Data Type
Use the ‘URL’ data type if you want Data Studio to expect a URL when processing a field in the specified data set.
Currency Data Type
Use the ‘Currency’ data type if you want Data Studio to expect a currency when processing a field in the specified data set.
Related Articles
- How to use Supermetrics for Google Sheets Add-on
- How to extract data from Google Analytics into Google Sheets
- How to extract data from Google Ads into Google Sheets
- How to extract data from Facebook Ads into Google Sheets
- How to extract data from Google Search Console into Google Sheets
- Guide to Supermetrics queries for Google Sheets
- How to extract data from Excel or CSV file into Google Sheets
- Google Data Studio Tutorial – 2020
- How to format Google Sheets data for Google Data Studio
- How to use Google Data Studio with Google Sheets
- Community visualization access in Google Data Studio
- Data Source version history in Google Data Studio
- Dimensions and metrics in Google Data Studio
- Field editing in reports – Google Data Studio
- Guide to data source fields in Google Data Studio
- Guide to data sources in Google Data Studio
- How to create and configure a data source in Google Data Studio
- How to share a data source with others in Google Data Studio
- Understanding data freshness in Google Data Studio
- Understanding data source credentials in Google Data Studio
- How to use Google Data Studio in another language
- Doing basic maths on numeric fields via calculated fields
- Google Data Studio number formats / data types
- Guide to calculated fields in Google Data Studio
- Guide to date and time data types in Google Data Studio
- How to work with text data type in Google Data Studio
- How to work with boolean data type in Google Data Studio
- The ‘data set configuration error’ in Google Data Studio
- Working with timezones in Google Sheets and Google Data Studio
- Formula rejection in Google Data Studio
- Understanding functions in Google Data Studio
- How to edit a calculated field in Google Data Studio
- Why you should avoid using functions and calculated fields in Data Studio
- Guide to data types in Google Data Studio
- Understanding aggregation in Google Data Studio
Do you know the difference between Web Analytics and Google Analytics?
99.99% of course creators themselves don’t know the difference between Web analytics, Google Analytics (GA) and Google Tag Manager (GTM).
So they are teaching GA and GTM in the name of teaching Web analytics.
They just copy each other. Monkey see, monkey do.
But Web analytics is not about GA, GTM.
It is about analyzing and interpreting data, setting up goals, strategies and KPIs.
It’s about creating strategic roadmap for your business.
Web Analytics is the core skill. Google Analytics is just a tool used to implement ‘Web Analytics’.
You can also implement ‘Web analytics’ via other tools like ‘adobe analytics’, ‘kissmetrics’ etc.
Using Google Analytics without the good understanding of ‘Web analytics’ is like driving around in a car, in a big city without understanding the traffic rules and road signs.
You are either likely to end up somewhere other than your destination or you get involved in an accident.
You learn data analysis and interpretation from Web analytics and not from Google Analytics.
The direction in which your analysis will move, will determine the direction in which your marketing campaigns and eventually your company will move to get the highest possible return on investment.
You get that direction from ‘Web analytics’ and not from ‘Google Analytics’.
You learn to set up KPIs, strategies and measurement framework for your business from ‘Web analytics’ and not from ‘Google Analytics’.
So if you are taking a course only on 'Google Analytics’, you are learning to use one of the tools of ‘Web analytics’. You are not learning the ‘Web analytics’ itself.
Since any person can learn to use Google Analytics in couple of weeks, you do no get any competitive advantage in the marketplace just by knowing GA.
You need to know lot more than GA in order to work in Web analytics and marketing field.
So what I have done, if you are interested, is I have put together a completely free training that will teach you exactly how I have been able to leverage web/digital analytics to generate floods of news sales and customers and how you can literally copy what I have done to get similar results.
Here what You'll Learn On This FREE Web Class!
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
The data type of a data source field determines the kind of data to expect (in the connected data set) when processing the field.
For example, when the data type of a field is ‘Number’, it tells Data Studio to expect a number when processing the field:
When the data type of a field is ‘Currency (US – Dollars) ’, it tells Data Studio to expect currency data in US dollars when processing the field:
Similarly, when the data type of a field is ‘Text’, it tells Data Studio to expect text data when processing the field:
The data type determines which operations are allowed and not allowed on a data source field.
Get the ebook on Google Data Studio (50+ Pages)
For example, you can’t apply an arithmetic function to a ‘Text’ field or use a ‘Number’ field as the date range dimension in a report. If you want to change the data type of a field then just click on the drop-down menu next to a data type.
Note: Changing the field type can have a considerable impact on how you see your data in reports.
Google Data Studio supports the following data types:
- Numeric
- Text
- Date and Time
- Boolean
- Geo
- Currency
- URL
Numeric data types
There are three numeric data types in Google Data Studio:
- Number – select this data type if you want Data Studio to expect a number (includes floating-point number) when processing the field in the specified data set.
- Percent – select this data type if you want Data Studio to expect percentage data when processing the field in the specified data set.
- Duration – select this data type if you want Data Studio to expect time duration in seconds when processing the field in the specified data set.
For example, consider the following data set:
The field ‘Number of orders’ is of type ‘number’.
The field ‘Percentage of Sales’ is of type ‘percent’.
The field ‘Duration’ is of type ‘duration’.
If we connect this data set to our data source schema then when deciding the data source schema (structure) we should:
- set the data type of the field ‘Number of orders’ to ‘Numbers’.
- set the data type of the field ‘Percentage of Sales’ to ‘Percent’.
- set the data type of the field ‘Phone Call Duration’ to ‘Duration’.
To learn more about numeric data types in Google Data Studio read the following two articles:
#1 Google Data Studio Number Formats / Data Types
#2 Doing Basic Maths on Numeric Fields via Calculated Fields
Text data type
Select the ‘Text’ data type if you want Data Studio to expect text when processing a field in the specified data set. A text data type can include any combination of letters, numbers, special symbols (like [, }, @….) and other characters.
Consider the following data set:
Here all the values of the field ‘Customers Name’ are of type ‘Text’. So when defining the data source schema, we would set the data type of the field ‘Customers Name’ to ‘Text’:
To learn more about the text data types, read this article: How to Work with ‘Text’ Data Type in Google Data Studio
Date and time data types
As you can see from the screenshot, Google Data Studio supports several different types of date and time.
The data types for ‘date’ fields can be divided into the following two categories:
- Absolute Dates
- Relative Dates
The data types for ‘time’ fields can be divided into the following two categories:
- Absolute time
- Relative time
Absolute date
An absolute date refers to a specific date that you can point to on a calendar. Following are examples of absolute dates in the context of Google Data Studio:
- 2019
- Q1 2019
- Nov 2019
- Nov 10, 2019
Absolute date types
These are the data types meant for absolute dates. Following are absolute date types:
You should select that data type for your absolute date which matches the date type in your data set.
Absolute time
An absolute time refers to a specific time that you can point to on a clock.
Following are examples of absolute time in the context of Google Data Studio:
- Sept 10, 2019, 10 PM
- Sept 10, 2019, 10:30 PM
Data types for absolute time
These are the data types meant for absolute times. Following are data types for absolute time:
You should select that data type for your absolute time that matches the time type in your data set.
Relative date
The relative date is the date that does not refer to a specific date.
Following are examples of relative dates in the context of Google Data Studio:
- Q1, Q2, Q3, Q4 – here we don’t know, we are referring to the quarter of which year.
- Jan, Feb, March, April… – here we don’t know, we are referring to the month of which year.
- Mon, Tue, Wed, Thu… – here we don’t know, we are referring to the day of which week or month or year.
Relative date types
These are the data types meant for relative dates. Following are relative date types:
You should select that data type for your relative date which matches the date type in your data set.
Relative time
Relative time is the time which does not refer to a specific point in time.
Following are examples of relative time in the context of Google Data Studio:
- 1 am, 3 am, 7 pm, etc – here we don’t know which day, month or year the time occurred.
Data types for relative time
These are the data types meant for relative times. Following are data types for relative time:
You should select that data type for your relative time which matches the time type in your data set.
To learn more about the date and time data types in Data Studio read this article: Tutorial on Date and Time Data Types in Google Data Studio
Boolean data type
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.
To learn more about working with the boolean data type, read this article: How to work with Boolean data type in Google Data Studio
Geo data types
Use ‘Geo’ data type if you want Data Studio to expect a geographic region (like a city, region, country, continent) when processing a field in the specified data set.
Following are the various Geo data types available in Google Data Studio:
To learn more about the geo data types, read this article: Geo Data – Country, Region, Latitude, Longitude in Google Data Studio
URL Data Type
Use the ‘URL’ data type if you want Data Studio to expect a URL when processing a field in the specified data set.
Currency Data Type
Use the ‘Currency’ data type if you want Data Studio to expect a currency when processing a field in the specified data set.
Related Articles
- How to use Supermetrics for Google Sheets Add-on
- How to extract data from Google Analytics into Google Sheets
- How to extract data from Google Ads into Google Sheets
- How to extract data from Facebook Ads into Google Sheets
- How to extract data from Google Search Console into Google Sheets
- Guide to Supermetrics queries for Google Sheets
- How to extract data from Excel or CSV file into Google Sheets
- Google Data Studio Tutorial – 2020
- How to format Google Sheets data for Google Data Studio
- How to use Google Data Studio with Google Sheets
- Community visualization access in Google Data Studio
- Data Source version history in Google Data Studio
- Dimensions and metrics in Google Data Studio
- Field editing in reports – Google Data Studio
- Guide to data source fields in Google Data Studio
- Guide to data sources in Google Data Studio
- How to create and configure a data source in Google Data Studio
- How to share a data source with others in Google Data Studio
- Understanding data freshness in Google Data Studio
- Understanding data source credentials in Google Data Studio
- How to use Google Data Studio in another language
- Doing basic maths on numeric fields via calculated fields
- Google Data Studio number formats / data types
- Guide to calculated fields in Google Data Studio
- Guide to date and time data types in Google Data Studio
- How to work with text data type in Google Data Studio
- How to work with boolean data type in Google Data Studio
- The ‘data set configuration error’ in Google Data Studio
- Working with timezones in Google Sheets and Google Data Studio
- Formula rejection in Google Data Studio
- Understanding functions in Google Data Studio
- How to edit a calculated field in Google Data Studio
- Why you should avoid using functions and calculated fields in Data Studio
- Guide to data types in Google Data Studio
- Understanding aggregation in Google Data Studio
Do you know the difference between Web Analytics and Google Analytics?
99.99% of course creators themselves don’t know the difference between Web analytics, Google Analytics (GA) and Google Tag Manager (GTM).So they are teaching GA and GTM in the name of teaching Web analytics.
They just copy each other. Monkey see, monkey do.
But Web analytics is not about GA, GTM.
It is about analyzing and interpreting data, setting up goals, strategies and KPIs.
It’s about creating strategic roadmap for your business.
Web Analytics is the core skill. Google Analytics is just a tool used to implement ‘Web Analytics’.You can also implement ‘Web analytics’ via other tools like ‘adobe analytics’, ‘kissmetrics’ etc.
Using Google Analytics without the good understanding of ‘Web analytics’ is like driving around in a car, in a big city without understanding the traffic rules and road signs.
You are either likely to end up somewhere other than your destination or you get involved in an accident.
You learn data analysis and interpretation from Web analytics and not from Google Analytics.The direction in which your analysis will move, will determine the direction in which your marketing campaigns and eventually your company will move to get the highest possible return on investment.
You get that direction from ‘Web analytics’ and not from ‘Google Analytics’.
You learn to set up KPIs, strategies and measurement framework for your business from ‘Web analytics’ and not from ‘Google Analytics’.So if you are taking a course only on 'Google Analytics’, you are learning to use one of the tools of ‘Web analytics’. You are not learning the ‘Web analytics’ itself.
Since any person can learn to use Google Analytics in couple of weeks, you do no get any competitive advantage in the marketplace just by knowing GA.
You need to know lot more than GA in order to work in Web analytics and marketing field.
So what I have done, if you are interested, is I have put together a completely free training that will teach you exactly how I have been able to leverage web/digital analytics to generate floods of news sales and customers and how you can literally copy what I have done to get similar results.
Here what You'll Learn On This FREE Web Class!
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