Data Source Fields in Google Data Studio – Tutorial
A data source schema is made up of a set of fields called the data source schema fields:
A connector is used to pull these fields from a data source.
Only the fields provided by the connector are the ones available to use in your schema and reports.
There are two broad categories of fields in Google Data Studio:
- Regular fields
- Calculated fields
Regular field
It is a field that doesn’t perform action(s) on other field(s) in your data source schema or chart.
Calculated field
It is a field that performs certain action(s) on other field(s) in your data source schema or chart via a formula. These actions could be:
- Arithmetic and math operations
- Manipulating text, date and geographic information
- Using branching logic to evaluate data
Through calculated fields, you can create new metrics and dimensions.
Regular fields can be further categorized into:
- Data Source Schema specific fields
- Chart Specific (or chart level) fields
Data Source Schema specific field
It refers to the regular field of a data source schema. The data source schema field is available in any report that uses that data source schema.
Chart Specific (or chart level) field
It refers to the regular field of a specific chart in a report. The chart specific field is not available to other charts of the same or other data studio reports.
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Data source schema specific fields can be further categorized into:
- Data source schema specific dimensions
- Data source schema specific metrics
Data source schema specific dimension
It refers to the regular dimension of a data source schema.
The regular dimension is the field which is used to describe or categorize your data. In the context of Google Analytics, dimension is one of the attributes of your website visitors.
The data source schema specific dimension is available in any report that uses that data source schema.
Data source schema specific metric
It refers to the regular metric of a data source schema.
The regular metric is the field which is used to measure one of the characteristics of a dimension. In the context of Google Analytics, metrics is a numeric field which is used to measure one of the characteristics of a GA dimension.
The data source schema specific metric is available in any report that uses that data source schema.
Chart specific (or chart level) fields can be further categorized into:
- Chart specific (or chart level) dimensions
- Chart specific (or chart level) metrics
Chart specific (or chart level) dimension
It refers to the regular dimension of a specific chart in a report.
The chart specific dimension is not available to the charts of same or other data studio reports.
Chart specific (or chart level) metric
It refers to the regular metric of a specific chart in a report.
The chart specific metric is not available to the charts of same or other data studio reports.
Following is the visual summary:
Calculated fields can be further categorized into:
- Data Source Schema specific calculated fields
- Chart Specific (or chart level) calculated fields
Data Source schema specific calculated field
It is the calculated field created in a data source schema.
When you create a calculated field in a data source schema, the calculated field is available in any report that uses that data source schema.
A data studio report which uses multiple data source schemas is based on multiple data sources:
Note: You can’t use a data source specific calculated field with blended data (more about blended data later).
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.
Note: When using a chart specific calculated field, you can not reference other chart-specific calculated fields in your formula, even if those fields are defined in the same chart. If you want to reference other calculated fields, use a data source schema specific calculated field.
Data source schema specific calculated fields can be further categorized into:
- Data source schema specific calculated dimensions
- Data source schema specific calculated metrics
Data source schema specific calculated dimension
It is the dimension that performs certain action(s) on other field(s) in your data source schema via a formula.
When you create a calculated dimension in a data source schema, the calculated dimension is available in any report that uses that data source schema.
Note: All calculated dimensions appear in the data source schema with an ‘fx’ symbol.
Data source schema specific calculated metric
It is the metric that performs certain action(s) on other field(s) in your data source schema via a formula.
When you create a calculated metric in a data source schema, the calculated metric is available in any report that uses that data source schema.
Note: All calculated metrics appear in the data source schema with an ‘fx’ symbol.
Chart specific calculated fields can be further categorized into:
- Chart specific calculated dimensions
- Chart specific calculated metrics
Chart specific calculated dimension
It is the dimension that performs certain action(s) on other field(s) in your chart via a formula.
When you create a calculated dimension in a chart, the dimension is available only in the chart in which you create it.
However, you can use a chart specific calculated dimension with blended data.
Chart specific calculated metric
It is the metric that performs certain action(s) on other field(s) in your chart via a formula.
When you create a calculated metric in a chart, the metric is available only in the chart in which you create it.
However, you can use a chart specific calculated metric with blended data.
Following is the visual summary:
The parameter field
While editing a data source schema, you get the option to add one or more parameter fields:
A parameter is a data source schema field whose value is supplied by a report user. It is used in calculated fields and report components just like a dimension or a metric.
Through parameters, you can pass user-supplied data to calculated fields and connectors. So if you want your report to display results based on user input then you use the parameter field.
You will find parameters at the bottom of the list of the available fields in your data source schema or report editor:
Note: Parameters appear as a purple field.
To learn more about parameters in data studio, read this article: How to create and use parameters in Google Data Studio.
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
- Image
- Image link
To learn more about the various data types in Google data studio, read this article: Guide to Data Types in Google Data Studio
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#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.
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#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.
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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.
About the Author
Himanshu Sharma
A data source schema is made up of a set of fields called the data source schema fields:
A connector is used to pull these fields from a data source.
Only the fields provided by the connector are the ones available to use in your schema and reports.
There are two broad categories of fields in Google Data Studio:
- Regular fields
- Calculated fields
Regular field
It is a field that doesn’t perform action(s) on other field(s) in your data source schema or chart.
Calculated field
It is a field that performs certain action(s) on other field(s) in your data source schema or chart via a formula. These actions could be:
- Arithmetic and math operations
- Manipulating text, date and geographic information
- Using branching logic to evaluate data
Through calculated fields, you can create new metrics and dimensions.
Regular fields can be further categorized into:
- Data Source Schema specific fields
- Chart Specific (or chart level) fields
Data Source Schema specific field
It refers to the regular field of a data source schema. The data source schema field is available in any report that uses that data source schema.
Chart Specific (or chart level) field
It refers to the regular field of a specific chart in a report. The chart specific field is not available to other charts of the same or other data studio reports.
Get the ebook on Google Data Studio (50+ Pages)
Data source schema specific fields can be further categorized into:
- Data source schema specific dimensions
- Data source schema specific metrics
Data source schema specific dimension
It refers to the regular dimension of a data source schema.
The regular dimension is the field which is used to describe or categorize your data. In the context of Google Analytics, dimension is one of the attributes of your website visitors.
The data source schema specific dimension is available in any report that uses that data source schema.
Data source schema specific metric
It refers to the regular metric of a data source schema.
The regular metric is the field which is used to measure one of the characteristics of a dimension. In the context of Google Analytics, metrics is a numeric field which is used to measure one of the characteristics of a GA dimension.
The data source schema specific metric is available in any report that uses that data source schema.
Chart specific (or chart level) fields can be further categorized into:
- Chart specific (or chart level) dimensions
- Chart specific (or chart level) metrics
Chart specific (or chart level) dimension
It refers to the regular dimension of a specific chart in a report.
The chart specific dimension is not available to the charts of same or other data studio reports.
Chart specific (or chart level) metric
It refers to the regular metric of a specific chart in a report.
The chart specific metric is not available to the charts of same or other data studio reports.
Following is the visual summary:
Calculated fields can be further categorized into:
- Data Source Schema specific calculated fields
- Chart Specific (or chart level) calculated fields
Data Source schema specific calculated field
It is the calculated field created in a data source schema.
When you create a calculated field in a data source schema, the calculated field is available in any report that uses that data source schema.
A data studio report which uses multiple data source schemas is based on multiple data sources:
Note: You can’t use a data source specific calculated field with blended data (more about blended data later).
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.
Note: When using a chart specific calculated field, you can not reference other chart-specific calculated fields in your formula, even if those fields are defined in the same chart. If you want to reference other calculated fields, use a data source schema specific calculated field.
Data source schema specific calculated fields can be further categorized into:
- Data source schema specific calculated dimensions
- Data source schema specific calculated metrics
Data source schema specific calculated dimension
It is the dimension that performs certain action(s) on other field(s) in your data source schema via a formula.
When you create a calculated dimension in a data source schema, the calculated dimension is available in any report that uses that data source schema.
Note: All calculated dimensions appear in the data source schema with an ‘fx’ symbol.
Data source schema specific calculated metric
It is the metric that performs certain action(s) on other field(s) in your data source schema via a formula.
When you create a calculated metric in a data source schema, the calculated metric is available in any report that uses that data source schema.
Note: All calculated metrics appear in the data source schema with an ‘fx’ symbol.
Chart specific calculated fields can be further categorized into:
- Chart specific calculated dimensions
- Chart specific calculated metrics
Chart specific calculated dimension
It is the dimension that performs certain action(s) on other field(s) in your chart via a formula.
When you create a calculated dimension in a chart, the dimension is available only in the chart in which you create it.
However, you can use a chart specific calculated dimension with blended data.
Chart specific calculated metric
It is the metric that performs certain action(s) on other field(s) in your chart via a formula.
When you create a calculated metric in a chart, the metric is available only in the chart in which you create it.
However, you can use a chart specific calculated metric with blended data.
Following is the visual summary:
The parameter field
While editing a data source schema, you get the option to add one or more parameter fields:
A parameter is a data source schema field whose value is supplied by a report user. It is used in calculated fields and report components just like a dimension or a metric.
Through parameters, you can pass user-supplied data to calculated fields and connectors. So if you want your report to display results based on user input then you use the parameter field.
You will find parameters at the bottom of the list of the available fields in your data source schema or report editor:
Note: Parameters appear as a purple field.
To learn more about parameters in data studio, read this article: How to create and use parameters in Google Data Studio.
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
- Image
- Image link
To learn more about the various data types in Google data studio, read this article: Guide to Data Types in Google Data Studio
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.
About the Author
Himanshu Sharma