Data Source Fields in Google Data Studio – Tutorial
A data source is made up of a set of fields called data source fields. You see these fields when you create and configure your data source in Google Data Studio:
Connectors provide fields from a data set to a data source.
Some connectors (like the Google Sheets connector) provide all the fields from a data set to a data source. Other connectors (e.g. Google Analytics connector, Google Ads connector, etc) provide only a subset of the fields from the data set to the data source.
Only the fields provided by the connector are the ones, available to use in your reports.
Types of data source fields
There are two broad categories of data source fields:
- Regular field – a data source field that doesn’t perform some action(s) on another field(s) in your data source.
- Calculated field – a data source field that performs some action(s) on another field(s) in your data source or chart via a formula.
Regular fields can be further categorized into:
- Regular dimension – It is the attribute of visitors to your website. It is used to describe or categorize your data.
- Regular metric – It is a number that is used to measure one of the characteristics of a dimension.
Get the ebook on Google Data Studio (50+ Pages)
Calculated fields can be further categorized into:
- Data Source specific calculated field – It is the calculated field created in a data source. When you create a calculated field in a data source, the calculated field is available in any report that uses that data source. However, you can’t use a data source specific calculated field with blended data.
- Chart specific (or chart level) calculated field – It is the calculated field created in a specific chart in a report. When you create a calculated field in a chart, the calculated field is available only in the chart in which you create it. However, you can use a chart specific calculated field with blended data.
Data Source specific calculated field can be further categorized into:
- Data source specific calculated dimension – It is a dimension that performs some action(s) on another field(s) in your data source via a formula.
- Data source specific calculated metric – It is a metric that performs some action(s) on another field(s) in your data source via a formula.
Chart specific calculated field can be further categorized into:
- Chart specific calculated dimension – It is a dimension that performs some action(s) on another field(s) in your chart via a formula.
- Chart specific calculated metric – It is a metric that performs some action(s) on another field(s) in your chart via a formula.
Introduction to fields data types
The data type of a data source field determines the kind of data to expect (in the specified 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.
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
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Master the Essentials of Email Marketing Analytics
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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.
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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.
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A data source is made up of a set of fields called data source fields. You see these fields when you create and configure your data source in Google Data Studio:
Connectors provide fields from a data set to a data source.
Some connectors (like the Google Sheets connector) provide all the fields from a data set to a data source. Other connectors (e.g. Google Analytics connector, Google Ads connector, etc) provide only a subset of the fields from the data set to the data source.
Only the fields provided by the connector are the ones, available to use in your reports.
Types of data source fields
There are two broad categories of data source fields:
- Regular field – a data source field that doesn’t perform some action(s) on another field(s) in your data source.
- Calculated field – a data source field that performs some action(s) on another field(s) in your data source or chart via a formula.
Regular fields can be further categorized into:
- Regular dimension – It is the attribute of visitors to your website. It is used to describe or categorize your data.
- Regular metric – It is a number that is used to measure one of the characteristics of a dimension.
Get the ebook on Google Data Studio (50+ Pages)
Calculated fields can be further categorized into:
- Data Source specific calculated field – It is the calculated field created in a data source. When you create a calculated field in a data source, the calculated field is available in any report that uses that data source. However, you can’t use a data source specific calculated field with blended data.
- Chart specific (or chart level) calculated field – It is the calculated field created in a specific chart in a report. When you create a calculated field in a chart, the calculated field is available only in the chart in which you create it. However, you can use a chart specific calculated field with blended data.
Data Source specific calculated field can be further categorized into:
- Data source specific calculated dimension – It is a dimension that performs some action(s) on another field(s) in your data source via a formula.
- Data source specific calculated metric – It is a metric that performs some action(s) on another field(s) in your data source via a formula.
Chart specific calculated field can be further categorized into:
- Chart specific calculated dimension – It is a dimension that performs some action(s) on another field(s) in your chart via a formula.
- Chart specific calculated metric – It is a metric that performs some action(s) on another field(s) in your chart via a formula.
Introduction to fields data types
The data type of a data source field determines the kind of data to expect (in the specified 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.
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
EXCLUSIVE FREE TRAINING REVEALS....
"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…
#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