Dimensions vs Metrics: Using Them in Google Data Studio
When you're starting with Data Studio, it's a little difficult to know what should be a dimension and what should be a metric.
Making the right choice will make your reports better and make you look like an expert. 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 fundamental units of all data sources.
You'll encounter 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. In a chart, you can use dimensions to categorize data. Campaign Name, Product ID, and Country are just a few examples of dimensions you might use to categorize data. A dimension can be any sort of data, including a column of unaggregated numbers.
Metrics are values that measure dimensions. In general, you might imagine that metrics are numeric values but they can be currencies or ratios as well. Another way of saying this is that a metric is the outcome of applying an aggregation to a group of values. That aggregation could be the result of the underlying data collection, or it could be the result of applying an aggregate function, such as COUNT(), SUM(), or AVG(), to the underlying data ().
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.
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 is displayed as a metric or dimension. More on this can be found here.
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, the calculated field will sum your costs into one field.
User-defined data is saved in parameters. When creating reports and data sources, you may use parameters, such as a BigQuery custom query parameter, to tailor or personalize them based on user input or variables described in the underlying data set.
For example, you want to include your monthly ad spend budget in your dashboard. You can create a parameter that carries your budget - let's say $5000 - and stores it as a constant value. When creating charts, you can use this parameter to show as is, or include it into a calculated filed formula. For more on parameters, you can check this tutorial.