Change data types in Data Studio

If a field in your report isn’t behaving or displaying as expected, learning how to change data types in Data Studio can help you fix it. A value may be showing as a plain number when it should be currency, a discount may need to appear as a percentage, or a city field may need to be identified as geographical data before you can use it in a map.

Data type is only one part of the field configuration. You can also change how a metric is aggregated and apply comparison calculations to show values such as percentage of total or difference from the highest result.

Let’s explore how I change data types in Data Studio, modify metric aggregations and add comparisons directly within a chart.

For the examples in this post, I’ll use the Superstore Products dataset.

Why change data types in Data Studio?

A field’s data type tells Data Studio what kind of information it contains and how that information should be processed and displayed.

For example:

• A currency field can display an appropriate currency symbol and number format.
• A percentage field can display decimal values as percentages.
• A date field can be used with date controls and time-based charts.
• A geographical field can be recognised correctly by maps.
• A URL field can be displayed as a clickable link.

If the selected type doesn’t match the underlying data, the field may display incorrectly or may not be available for the operation or chart you want to use.

1. How to change data types in Data Studio

When you create a numeric calculated field, Data Studio will normally treat its output as a number. However, that may not be the most appropriate display type.

For example, some of the calculated metrics we created using the Superstore Products dataset represent monetary values. Instead of showing them as plain numbers, it makes more sense to display them as currency.

Here’s how I change the data type at data-source level:

1. Go to Resource.
2. Select Manage added data sources.
3. Click Edit beside the relevant data source.
4. Find the field you want to update.
5. Open the dropdown in the Type column.
6. Select the required type and format.

For a monetary metric, select Currency and then choose the specific currency you want to use.

Remember that changing a field at data-source level can affect every chart and report using that field. If you only want to change how it appears in one chart, you can edit the field directly within that chart instead.

Change a number to a percentage

You can also change data types in Data Studio for fields you didn’t create yourself.

For example, the Discount field may initially be recognised as a regular number. However, because the value represents a proportion, it would make more sense to display it as a percentage.

Open the field’s type menu and select the appropriate percentage option. Data Studio will then format the values accordingly.

Before making this change, check how the original values are stored. A value of 0.2 would normally be displayed as 20%, while a source value already stored as 20 may need to be adjusted before applying percentage formatting.

Change text to geographical data

Data-type changes also apply to dimensions.

For example, a City dimension may initially be recognised as text. If you want to use it in a map, you may need to change its type to geographical information and select City as the geographical level.

This is particularly important because map visualisations expect a recognised geographical dimension. A text field containing city names may not automatically behave as a location.

Changing the type helps Data Studio understand how the field should be used, although you should still check that ambiguous city names are being plotted in the correct country.

Change a field within an individual chart

You don’t always need to edit the original data source to change data types in Data Studio.

If field editing is enabled, you can select a chart and edit one of its dimensions or metrics from the Set-up panel. Click the field’s type or aggregation icon to open its editing options.

From there, you can change:

• The display name
• The data type
• The metric aggregation
• The comparison calculation
• The running calculation
• The number or date display format

Changes made this way only affect that instance of the field in the selected chart. The original field and any other charts using it remain unchanged.

2. Change metric aggregation in Data Studio

You may notice that some numerical fields, such as Gross Revenue, can be used as either dimensions or metrics. Revenue is clearly a quantitative value, so why doesn’t it always behave like a predefined metric?

For data sources such as Google Sheets, CSV files and BigQuery tables, raw numerical fields are commonly given a default aggregation such as Sum. This allows the same field to be used in different ways within a report.

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Note: This can behave differently with connectors such as Google Analytics, where many metrics are already defined and may use an automatic aggregation that can’t be changed.

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The advantage of flexible numerical fields is that you can change how they are aggregated within an individual chart.

Common aggregation options include:

• Sum
• Average
• Count
• Count Distinct
• Minimum and Maximum
• Median
• Variance and Standard deviation

Change Gross Revenue from Sum to Average

Imagine that you want to display the average Gross Revenue value instead of the total.

In the chart’s Set-up panel, click the aggregation label shown beside the metric, such as SUM.

Select Average as the aggregation type for Gross Revenue.

The result is the average Gross Revenue for the rows included at the chart’s current level of detail. In this dataset, that may mean the average per transaction and product combination.

This is worth distinguishing from a true Average Order Value, which normally means total revenue divided by the number of distinct orders. If one order can contain several product rows, averaging Gross Revenue by row won’t necessarily produce the same result.

However, if your data contains exactly one row per order, changing the aggregation to Average can provide the average order value directly.

Rename the edited metric

When changing a metric’s aggregation, I recommend renaming it so viewers understand what the new value represents.

For example, you could rename Gross Revenue to Average Gross Revenue or Average Order Value when that name accurately describes the calculation.

The original field name remains available under Source field, so you can still see that the result is based on Gross Revenue.

Once you rename the metric in the field-editing screen, the new display name appears in that chart’s configuration.

Count unique customers

You can use the same options to calculate the number of customers.

Add Customer ID as a metric and select Count Distinct as its aggregation.

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Quick tip:

• Count counts all non-null Customer ID records, including repeated IDs.
• Count Distinct counts each unique Customer ID only once.

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3. Change comparison calculations

Another useful change you can make within a chart is applying a comparison calculation.

Imagine that a table displays total Gross Revenue by product category. In addition to the revenue amount, you may want to show what percentage of the overall revenue each category represents.

To do this:

1. Add Gross Revenue to the chart as a second metric.
2. Open the second metric’s editing options.
3. Under Comparison calculation, select Percent of total.

Percent of total divides the value in each row by the grand total. This lets viewers see both the monetary value and the contribution each category makes to overall revenue.

Difference from total

You can also select Difference from total to compare each category with the grand total.

The related Percent difference from total option expresses that difference as a percentage of the total.

These calculations compare every row with the overall total, rather than with the category that has the highest revenue.

Difference from the highest category

If you want to know how far each category is from the category with the highest revenue, use one of the comparison-to-maximum options:

• Percent of max shows each category as a percentage of the highest value.
• Difference from max shows the numerical difference between each category and the highest value.
• Percent difference from max expresses that difference as a percentage of the highest value.

Which option you choose depends on whether you want to compare a row with the overall total or with the largest individual value.

Data-source changes vs chart-level changes

When you change data types in Data Studio, it’s important to decide where the change should be made.

• Data-source level: Use this when the field should always have the same type or default aggregation. The change can affect every chart and report using that data source.
• Chart level: Use this when you only want to change one instance of the field. The other charts and the original data source remain unchanged.

For example, changing Discount to a percentage at data-source level makes sense if it should always be displayed that way. Changing Gross Revenue from Sum to Average may be better at chart level if most other charts still need the total.

Best practices when changing field properties

To keep my reports accurate and easy to understand, I follow a few simple principles:

• Check the underlying values: Formatting a field as a percentage or currency doesn’t change what the source data means.
• Choose the right editing level: Use data-source changes for consistent definitions and chart-level changes for individual use cases.
• Rename modified metrics: The displayed name should explain the aggregation or calculation being shown.
• Check the chart’s level of detail: Average values depend on the dimensions and rows included in the chart.
• Distinguish Count from Count Distinct: Decide whether repeated identifiers should be counted more than once.
• Validate comparison calculations: Check whether you’re comparing with the total or with the maximum value.
• Test geographical fields: Make sure locations are recognised and plotted correctly after changing their type.

Even a small change to a field’s type or aggregation can significantly alter the meaning of a chart, so I always validate the result before sharing the report.

Final thoughts on how to change data types in Data Studio

Learning how to change data types in Data Studio gives you much more control over how fields behave and appear in your reports.

You can display numbers as currency or percentages, identify text fields as geographical data and change the way metrics are aggregated within individual charts.

Comparison calculations can also add valuable context by showing each category’s percentage of the total or its difference from the highest result.

The key is to understand what the underlying field represents and choose a type, aggregation and calculation that communicate that meaning accurately.

Ready to create and modify your metrics? I hope so! As always, feel free to leave a comment if you have any questions 🙂