Dimensions and metrics in Google Data Studio
Types of dimensions in Google Data Studio
There are four types of dimensions available in Data Studio:
#1 Data source schema specific dimension – It refers to the regular dimension of a data source schema. This dimension does not perform certain action(s) on other field(s) in your data source schema but is available in any report that uses that data source schema.
#2 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. This calculated dimension is available in any report that uses that data source schema.
#3 Chart specific 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 other Data Studio reports.
#4 Chart specific calculated dimension – It is the dimension that performs certain action(s) on other field(s) in your chart via a formula. This calculated dimension is available only in the chart in which you create it. However, you can use a chart specific calculated dimension with blended data.
#1 Data source schema specific dimensions
A data source schema specific dimension is the data source schema field which is used to describe or categorize the data in the underlying data source.
For example, consider the following data source:
This data source is described by the following categories:
- Order Date
- Transaction ID
- Customer Name
- Customer Email
- Product Purchased
- Product Category
- Product Brand
- Revenue
- Tax
- Shipping
- Country
- Region
- City
These are all dimensions:
A connector is used to pull these fields from a data source.
The data source schema specific dimensions are available in any report that uses that data source schema.
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What are data source schema specific dimensions in the context of Google Analytics?
In the context of Google Analytics, a dimension is one of the attributes of your website visitors.
If you are using Google Analytics as a data source then following are the examples of data source schema specific dimensions:
In order to understand the concept of Data Source Schema specific dimensions in the context of Google Analytics, consider the following example:
Let’s say a man aged between 25-34 from London visited your website after clicking on an organic search listing on Google which he found by searching for the keyword ‘attribution modelling’.
Let us also assume that he visited your website via a Chrome browser which is installed on a desktop computer that runs windows.
Now following are the attributes of the visitor to your website along with their values:
Gender – male
Age – 25-34
City – London
Source / Medium – Google / Organic
Keyword – Attribution Modelling
Browser – Chrome
Device Category – desktop
Operating System – Windows
Here Gender, Age, City, Source /Medium, Keyword, Browser, Device Category and Operating System are all examples of dimensions because they are the characteristics of your website users.
Here is how these dimensions may appear in your data source schema:
#2 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.
#3 Chart specific dimensions
A chart specific dimension refers to the regular dimension of a specific chart in a report. The chart specific dimension is not available to the charts of other Data Studio reports:
#4 Chart specific calculated dimensions
A chart specific calculated dimension is the dimension that performs certain action(s) on other field(s) in your chart via a formula.
This calculated dimension is available only in the chart in which you create it. However, you can use a chart specific calculated dimension with blended data:
The dimensions in your data source schema appear as green fields whereas metrics usually appear as blue fields:
Types of metrics in Google Data Studio
There are four types of metrics available in Data Studio:
#1 Data source schema specific metric – It refers to the regular metric of a data source schema. This metric does not perform certain action(s) on other field(s) in your data source schema but is available in any report that uses that data source schema.
#2 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. This calculated metric is available in any report that uses that data source schema.
#3 Chart specific 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 other Data Studio reports.
#4 Chart specific calculated metric – It is the metric that performs certain action(s) on other field(s) in your chart via a formula. This calculated metric is available only in the chart in which you create it. However, you can use a chart specific calculated metric with blended data.
#1 Data source schema specific metrics
A data source schema specific metric is the data source schema field which is used to measure one of the characteristics of a data source schema specific dimension.
The data source schema specific metrics are available in any report that uses that data source schema.
What are data source schema specific metrics in the context of Google Analytics?
In the context of Google Analytics, a metric is a numeric field which is used to measure one of the characteristics of a dimension.
If you are using Google Analytics as a data source then following are the examples of data source schema specific metrics:
A Google Analytics dimension can have one or more characteristics.
For example, the following are the characteristics of the GA dimension called ‘Source / Medium’:
- Sessions
- % New Sessions
- New Users
- Bounce Rate
- Pages / Sessions
- Avg. Session Duration
- Goal Conversion Rate
- Goal Completions
- Goal Value
Here, Sessions, % New Sessions, New Users, Bounce Rate, Pages / Sessions, etc are all examples of metrics because they are the characteristics of the dimension called ‘Source / Medium’
#2 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.
#3 Chart specific 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 other Data Studio reports.
#4 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.
Difference between dimensions and metrics in Google Analytics and Google Data Studio
In Google Analytics, a dimension cannot be used as a metric and vice versa. Whereas, in Google Data Studio, a dimension can be used as a metric and vice versa.
For example in the data table below, ‘age’ (which is defined as a dimension in GA) is used as a metric:
And you can see how Data Studio is reporting the values of ‘age’ field in the data table.
But when you use ‘age’ as a dimension, you see the correct data in the data table:
In Google Analytics, certain dimensions and metrics can not be used/queried together. However, this is not the case with Google Data Studio.
For example, in Google Analytics you can not use/query ‘user type’ dimension with Adwords ‘impressions’ metric.
In Data Studio you can do that but you are likely to see ‘User Configuration Error’:
When you are using Google Analytics dimensions and metrics in Google Data Studio, you need to be extra careful.
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"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
Types of dimensions in Google Data Studio
There are four types of dimensions available in Data Studio:
#1 Data source schema specific dimension – It refers to the regular dimension of a data source schema. This dimension does not perform certain action(s) on other field(s) in your data source schema but is available in any report that uses that data source schema.
#2 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. This calculated dimension is available in any report that uses that data source schema.
#3 Chart specific 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 other Data Studio reports.
#4 Chart specific calculated dimension – It is the dimension that performs certain action(s) on other field(s) in your chart via a formula. This calculated dimension is available only in the chart in which you create it. However, you can use a chart specific calculated dimension with blended data.
#1 Data source schema specific dimensions
A data source schema specific dimension is the data source schema field which is used to describe or categorize the data in the underlying data source.
For example, consider the following data source:
This data source is described by the following categories:
- Order Date
- Transaction ID
- Customer Name
- Customer Email
- Product Purchased
- Product Category
- Product Brand
- Revenue
- Tax
- Shipping
- Country
- Region
- City
These are all dimensions:
A connector is used to pull these fields from a data source.
The data source schema specific dimensions are available in any report that uses that data source schema.
Get the ebook on Google Data Studio (50+ Pages)
What are data source schema specific dimensions in the context of Google Analytics?
In the context of Google Analytics, a dimension is one of the attributes of your website visitors.
If you are using Google Analytics as a data source then following are the examples of data source schema specific dimensions:
In order to understand the concept of Data Source Schema specific dimensions in the context of Google Analytics, consider the following example:
Let’s say a man aged between 25-34 from London visited your website after clicking on an organic search listing on Google which he found by searching for the keyword ‘attribution modelling’.
Let us also assume that he visited your website via a Chrome browser which is installed on a desktop computer that runs windows.
Now following are the attributes of the visitor to your website along with their values:
Gender – male
Age – 25-34
City – London
Source / Medium – Google / Organic
Keyword – Attribution Modelling
Browser – Chrome
Device Category – desktop
Operating System – Windows
Here Gender, Age, City, Source /Medium, Keyword, Browser, Device Category and Operating System are all examples of dimensions because they are the characteristics of your website users.
Here is how these dimensions may appear in your data source schema:
#2 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.
#3 Chart specific dimensions
A chart specific dimension refers to the regular dimension of a specific chart in a report. The chart specific dimension is not available to the charts of other Data Studio reports:
#4 Chart specific calculated dimensions
A chart specific calculated dimension is the dimension that performs certain action(s) on other field(s) in your chart via a formula.
This calculated dimension is available only in the chart in which you create it. However, you can use a chart specific calculated dimension with blended data:
The dimensions in your data source schema appear as green fields whereas metrics usually appear as blue fields:
Types of metrics in Google Data Studio
There are four types of metrics available in Data Studio:
#1 Data source schema specific metric – It refers to the regular metric of a data source schema. This metric does not perform certain action(s) on other field(s) in your data source schema but is available in any report that uses that data source schema.
#2 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. This calculated metric is available in any report that uses that data source schema.
#3 Chart specific 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 other Data Studio reports.
#4 Chart specific calculated metric – It is the metric that performs certain action(s) on other field(s) in your chart via a formula. This calculated metric is available only in the chart in which you create it. However, you can use a chart specific calculated metric with blended data.
#1 Data source schema specific metrics
A data source schema specific metric is the data source schema field which is used to measure one of the characteristics of a data source schema specific dimension.
The data source schema specific metrics are available in any report that uses that data source schema.
What are data source schema specific metrics in the context of Google Analytics?
In the context of Google Analytics, a metric is a numeric field which is used to measure one of the characteristics of a dimension.
If you are using Google Analytics as a data source then following are the examples of data source schema specific metrics:
A Google Analytics dimension can have one or more characteristics.
For example, the following are the characteristics of the GA dimension called ‘Source / Medium’:
- Sessions
- % New Sessions
- New Users
- Bounce Rate
- Pages / Sessions
- Avg. Session Duration
- Goal Conversion Rate
- Goal Completions
- Goal Value
Here, Sessions, % New Sessions, New Users, Bounce Rate, Pages / Sessions, etc are all examples of metrics because they are the characteristics of the dimension called ‘Source / Medium’
#2 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.
#3 Chart specific 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 other Data Studio reports.
#4 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.
Difference between dimensions and metrics in Google Analytics and Google Data Studio
In Google Analytics, a dimension cannot be used as a metric and vice versa. Whereas, in Google Data Studio, a dimension can be used as a metric and vice versa.
For example in the data table below, ‘age’ (which is defined as a dimension in GA) is used as a metric:
And you can see how Data Studio is reporting the values of ‘age’ field in the data table.
But when you use ‘age’ as a dimension, you see the correct data in the data table:
In Google Analytics, certain dimensions and metrics can not be used/queried together. However, this is not the case with Google Data Studio.
For example, in Google Analytics you can not use/query ‘user type’ dimension with Adwords ‘impressions’ metric.
In Data Studio you can do that but you are likely to see ‘User Configuration Error’:
When you are using Google Analytics dimensions and metrics in Google Data Studio, you need to be extra careful.
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
