Dimensions vs Metrics: Using Them in Google Data Studio

By Zekeriya Mulbay
Table of contents

What are Dimensions and Metrics?

Dimension and metrics are 2 main data source types or field types. All data sources have these 2 main types of data fields.

|   Dimensions and metrics are fundemental units of all data sources.

You'll encounter into metrics and dimensions whether you're using Google Data Studio, Google Analytics, Adobe Experience Cloud, Excel, or any other piece of software that allows you to turn raw analytics data into charts and graphs - and they'll be puzzling at first. Your first few graphs will most likely be nonsense. That's all right! It isn't the most natural thing on the planet.


*Dimensions describe or categorize the data. *****When you add dimensions to a chart, the data is organized by those dimensions. Campaign name, Product ID, and Country are just a few examples of dimensions you may use to categorize data in a chart. A dimension can be any sort of data, including a column of unaggregated numbers.


Metrics are values which measure dimensions. In general you might imagine that metrics are numeric values but they can be currencies or ratios as well. In other words, the result of applying an aggregation to a set of values is a metric. That aggregation could come from the underlying data collection, or it could be the consequence of applying an aggregate function, such as COUNT(), SUM(), or AVG().

Since we are here, let's check what are other field types. There are calculated fields and parameters. We will examine them more in the future.

Calculated Fields

They're fields you make by manipulating your data with functions, operators, and/or branching logic. Depending on the formula's output, a calculated field displays as metric or dimension.

Eventually all of us will use calculated fields. They work like a magic when you want to combine variables from 2 different data sources. For example you might want to see your total ad spend from Facebook and Google Ads, calculated field will sum your costs in to one field.


User-defined data is saved in parameters. You can utilize parameters, such as a BigQuery custom query parameter, to customize or personalize your reports and data sources based on user input or variables described in the underlying data set.

For example you want to include your monthly ad spend budget into your dashboard. You can create a parameter which carries your budget - let's say $5000 - and stores as a constant value. When creating charts, you can use this parameter to show as is, or include it into a calculated filed formula.

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