Data Studio scorecards

If you want to display your most important metrics clearly, Data Studio scorecards are one of the best places to start. They allow viewers to see a key value or Key Performance Indicator (KPI) immediately, without having to interpret a more complex chart.

Scorecards may look simple, but they include several useful configuration and customisation options. You can add comparisons, targets, sparklines and progress indicators, making them much more informative than a single number on a page.

Let’s explore how I create and customise Data Studio scorecards using data from the top movies of 2019.

Follow the steps in my post about adding data sources in Data Studio to add the dataset to your report, so you can replicate the examples shown here.

How to create Data Studio scorecards

For this example, we’re going to create a scorecard using data from the top movies of 2019. The movies in this dataset are ranked according to the revenue they generated, so revenue is the metric we’re going to display.

Here’s how I do it.

1. Click Add a chart.
2. Select the scorecard visualisation.
3. Move the cursor over the report canvas and click where you want to place the chart.

After placing the scorecard, you can start configuring it in the Set-up section of the Properties panel, which appears on the right-hand side of the canvas.

The value you want to display must be selected in the Metric field. In this case, we’re interested in the total revenue generated by the movies, so we’ll select the revenue metric.

Use the date range dimension

You’ll also see a field called Date range dimension. This field is important when you want the scorecard to display data for a specific period.

For example, imagine that you only want to see the revenue generated by movies released during the first three months of the year. To do this, use Release Date as the date range dimension.

This field may already be selected by default. If it isn’t, you can add it manually. You can then change the scorecard’s default date range to a custom period and select the first quarter of 2019.

The total revenue shown in the scorecard will automatically update to include only movies released within those dates.

You can read more about this in my post about the date range control in Data Studio.

In short, the date field selected as the date range dimension is the one Data Studio filters when you assign specific dates to the chart or use a date range control.

Imagine that your dataset contains two date fields: a US release date and a Spain release date. The field you select under Date range dimension will determine which release dates are filtered when you limit the scorecard to a specific period.

Customisation options for Data Studio scorecards

Once you’ve added your metric and configured the date range, you can use the additional options available in the Set-up and Style panels to give viewers more context.

Let’s take a closer look at the main options.

Add a sparkline to your scorecard

In the Set-up section, you’ll find an option to add a sparkline. A sparkline is a small line chart displayed within the scorecard that shows how the metric changes over time.

If you select Release Date as the sparkline dimension, the line will show the revenue according to the dates on which the movies were released.

The main scorecard value still shows the total revenue, while the sparkline gives viewers a quick indication of how that revenue is distributed over time.

In short, a sparkline adds useful context without taking up the space of a separate time series chart.

Compare the metric with a target value

Under the sparkline settings, you’ll find Other Comparison Options. These options allow you to compare the scorecard metric with another metric, a fixed value or a previous period.

Let’s compare the total movie revenue with a fixed target value of 5.5 billion.

Once the value is added, the scorecard displays the difference between the movie revenue and the target. In this example, revenue is 8% higher than the target value we entered.

This is particularly useful when using Data Studio scorecards to monitor KPIs. Instead of only seeing the current result, viewers can immediately understand whether performance is above or below the target.

Show the comparison as progress

Below the field where you entered the target of 5.5 billion, you’ll see the option to Show as progress.

If you activate this option, a progress bar will appear in the scorecard to show how the metric compares with the target value.

The sparkline will disappear, as the sparkline and progress visual cannot be displayed at the same time.

This gives viewers a quick visual indication of how close the metric is to the target, without requiring them to calculate the difference themselves.

Compare the result with a previous period

You can also compare the scorecard value with another time period.

Within Other Comparison Options, choose a period comparison and add a comparison period within Default date range.

For this example, I’ve selected 1–31 March 2019 as the default period and Previous period as the comparison period. Data Studio will therefore compare March with the previous month.

Period comparisons are useful when you want to monitor changes month over month, quarter over quarter or year over year. They make it easier to understand whether a KPI is improving or declining.

Display progress as a circle

If you go to the Style panel and find Progress visual, you’ll see a dropdown menu that allows you to change the progress bar into a circle.

Both options communicate the same information, so the best choice will depend on the design of your report and the space available on the page.

Use compact numbers for large values

Also within the Style panel, you’ll find the option to display Compact numbers.

When this option is selected, Data Studio displays large values in a shortened format. For example, a value measured in billions may be displayed using a B.

I like using compact numbers when working with very large values because they make Data Studio scorecards easier to read and prevent the main metric from taking up too much space.

Best practices for Data Studio scorecards

To ensure my scorecards are clear and easy to understand, I follow a few simple principles:

• Focus on important KPIs: Use scorecards for metrics that deserve immediate attention, rather than displaying every available metric in the same format.
• Provide context: Add a target, previous-period comparison or sparkline so viewers know whether the result is positive, negative or changing over time.
• Use clear labels: Make sure the metric name explains exactly what the value represents.
• Keep the design consistent: Scorecards that belong to the same group should use consistent fonts, sizes, spacing and comparison styles.
• Avoid overcrowding the page: Too many scorecards can make it difficult to identify which KPIs are most important.

Even small changes, such as adding a clear comparison label or shortening a large value, can make your scorecards much easier to interpret.

Final thoughts on Data Studio scorecards

Data Studio scorecards may be simple visualisations, but they offer many useful options for presenting KPIs clearly.

Sparklines show how a metric changes over time, comparison values provide context, and progress visuals make it easy to monitor performance against a target.

They’re particularly effective near the top of a dashboard, where viewers can quickly see the most important results before exploring the more detailed charts below.

Give these options a try, and see how much more context you can add to a simple scorecard.

As always, your comments and questions are more than welcome!

FAQs – Data Studio scorecards