Understanding the (Other) Row in Google Analytics 4: Expanding Insights with Expanded Data Sets
Google Analytics 4 (GA4) can be a powerful tool for understanding user behaviour and site performance. However, within its reports and explorations, users commonly encounter the mysterious “(other)” row. But what exactly is it, and how can expanded data sets be used to uncover deeper insights?
Understanding the (Other) Row
The “(other)” row emerges when the number of rows in a table surpasses its designated row limit, which can often not be sufficient. Essentially, when GA4 encounters this limit, it showcases only the most prevalent dimension values and consolidates less common ones under the “(other)” row.
For instance, suppose your website has 150k unique pages, yet the row limit for the Pages and Screens report stands at 100k. In that case, GA4 will arrange the rows from most to least common and amalgamate the last 50k rows under the “(other)” row. This causes a lot of confusion for our clients when they do not know what this data is and can lead to mistrust in the data.
Why Does It Appear?
The cardinality of dimensions plays a crucial role. Each dimension in GA4 can possess various assigned values. When a dimension holds an extensive array of values (high cardinality dimensions), the likelihood of encountering the “(other)” row amplifies. A dimension is typically considered high cardinality if it possesses more than 500 values.
If a report involves multiple dimensions, the potential row count multiplies based on the number of values for each dimension. For instance, a report comprising only Device (with 3 values) and Browser (with 7 values) has the potential to generate up to 21 rows. Whereas reports utilising high cardinality dimensions could easily reach thousands, or even millions, of rows.
Best Practices to Mitigate the (Other) Row
The “(other)” row occurring frequently can cause big problems for clients and their users. But there are some best practices that can be followed to help reduce this likelihood:
- Leverage Existing Dimensions: Use predefined dimensions rather than creating custom ones, especially if they serve the same purpose.
- Be Wary of High-Cardinality Dimensions: Using dimensions with an extensive array of values should be limited, as they can push your property closer to, or beyond, row limits.
- Avoid Unique Identifiers in Custom Dimensions: Instead, employ the User-ID feature for distinct user identification.
- Opt for Standard Reports: These reports employ aggregate tables, reducing the likelihood of data getting condensed.
- Channel High-Cardinality Data Wisely: Consider routing high-cardinality data through event parameters or user properties without registering them as custom dimensions, allowing use without affecting property limits.
More can be read about the “(other)” row in Google’s official documentation.
Leveraging Expanded Data Sets
Despite following all of these best practices, the “(other)” row can still get in the way of regular reporting and cause issues for clients. However, with a little work, it is possible to find out more about these rows of data with the use of expanded data sets which allow users to elevate the row limit in reports or explorations to a maximum of 2 million rows, significantly enhancing reporting capabilities.
Limits and Caveats of Expanded Data Sets
- Availability: Limited to Google Analytics 360 customers only.
- Quantity: Up to 100 expanded data sets per GA4 360 property.
- Usage: Recommended for ongoing detailed data needs rather than one-off reporting.
- Eligibility: Some reports, like Advertising reports, aren’t currently supported for expanded data — though this may change at some point.
How to Harness Expanded Data Sets
Requesting and managing expanded data sets involves a few key steps:
1. Requesting: Access an eligible report, click the data quality icon, and select “Expand this data.” Follow the prompts to create an expanded data set.
2. Managing: Navigate to Admin and select “Expanded data sets” to review and delete any sets not in use.
Final Thoughts
It is likely the “(other)” row may continue to cause confusion and pain for users, especially when it appears as the most common row in a report. However, understanding its nuances and the use of expanded data sets is a helpful workaround to allow for improved depth and accuracy of GA4 reporting and richer analytics.
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