Last Updated: October 16, 2022

Google Data Studio Dimension vs Metric in a nutshell

#1 Google Data Studio provides the following types of dimensions:

1. Data source schema specific dimension
2. Data source schema specific calculated dimension
3. Chart specific dimension
4. Chart specific calculated dimension

#2 Google Data Studio provides the following types of metrics:

1. Data source schema specific metric
2. Data source schema specific calculated metric
3. Chart specific metric
4. Chart specific calculated metric

#3 What are data source schema specific dimensions in the context of Google Analytics?

#4 What are data source schema specific metrics in the context of Google Analytics?

#5 Difference between dimensions and metrics in Google Analytics and 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 Brand
• Revenue
• Tax
• Shipping
• Country
• 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 the 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 another 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 cannot 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.

Frequently Asked Questions About Google Data Studio Dimension vs Metric

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