What Does Row Google Analytics 4 Mean?

Seeing "(other)" or "(not set)" listed in your Google Analytics 4 reports can be incredibly frustrating. These mysterious rows cloud your data, making it difficult to understand campaign performance, user behavior, or traffic sources. This guide cuts through the confusion, explaining exactly what "(other)" and "(not set)" mean, why they appear, and most importantly, how to fix them so you can trust your data again.

What is the "(other)" Row in Google Analytics 4?

First, let's be clear: the "(other)" row is not an error. It's a feature of how GA4 processes huge amounts of data. In short, the "(other)" row appears when you run into cardinality limits. Cardinality is simply the number of unique values a dimension can have. For example, the "Device category" dimension has low cardinality because of only a few possible values: Desktop, Mobile, and Tablet. Conversely, a dimension like "Page path" can have thousands or even millions of values on a large website, making it a high-cardinality dimension.

Each GA4 property has a limit on the number of unique dimension values it can process in a single table for its standard reports. When that limit is reached, Google groups all the less common values together and buckets them into the "(other)" row to ensure the report loads quickly. Think of it like a library trying to catalog books by title. If it only has 10,000 index cards, but it receives 11,000 unique book titles, it will log the most popular 10,000 and throw the remaining 1,000 on a single shelf labeled "(other)." That’s exactly what GA4 is doing with your data.

Common Causes of "(other)" in Your GA4 Reports

You’ll most often see the "(other)" row when looking at dimensions that are naturally high in cardinality. Pinpointing the source is the first step toward minimizing it.

High-Cardinality Dimensions like Page Path or Page Location: On large e-commerce or content sites, unique URLs can quickly pile up, especially if they contain unique IDs or parameters. For example, a URL like /products/item?id=12345 is unique for every product shown. With thousands of products, this dimension will easily exceed the limit.
Using User IDs: If you use the User ID feature to track logged-in users, the "User ID" dimension will have a unique value for every single user. It's designed to be a high-cardinality dimension, so looking at it in standard reports alongside other dimensions can quickly trigger the "(other)" row.
Improperly Configured Custom Dimensions: This is a massive culprit. A common mistake is capturing values that should never be dimensions. For instance, sending a user's unique session_id, transaction_id, or a granular timestamp as a custom dimension will create millions of unique values, blowing past cardinality limits almost instantly.
Automated Bot Traffic: Bots can generate junk traffic with randomized values in page paths or event parameters, needlessly inflating the number of unique values GA4 has to process and contributing to the "(other)" row.

How to Fix or Minimize the "(other)" Row in GA4

While you might not eliminate it completely, you can significantly reduce the impact of the "(other)" row and get cleaner data.

1. Use Exploration Reports

Standard baked-in GA4 reports (like the Pages and screens report) are pre-aggregated and have stricter cardinality limits. Exploration reports, on the other hand, are designed for deeper, ad-hoc analysis. They work with unprocessed data and have much higher sampling and cardinality limits. If you see "(other)" in a standard report, try building the same report in the "Explore" tab. In most cases, the "(other)" row will disappear, giving you the full, un-grouped data.

2. Be Smarter with Custom Dimensions

Audit your custom dimensions and events. Are you collecting data that is too granular? Never use values like session IDs, user IDs, timestamps, or product SKUs as a custom dimension. Instead, think in terms of broader categories.

Bad: Custom dimension for product_sku with thousands of unique values.
Good: Custom dimension for product_category (e.g., "Shoes," "Shirts," "Jackets").
Bad: Custom dimension for a precise timestamp of an action.
Good: Custom dimension for the time of day (e.g., "Morning," "Afternoon," "Evening").

3. Use Reporting Identity Settings Strategically

Go to Admin > Data Settings > Reporting Identity. GA4 uses a "Blended" model by default, which combines data from Device ID, Google Signals, and User ID. This can increase cardinality. Switching to an "Observed" or "Device-based" model can sometimes reduce the appearance of the "(other)" row in your reports because you're telling GA4 to stitch user journeys together using fewer unique identifiers.

4. Connect GA4 to BigQuery

This is the ultimate solution for dealing with cardinality forever. You can link your GA4 property directly to Google BigQuery (Google's data warehouse), often for free depending on an adequate volume of your events. This exports all the raw, hit-level event data from your site or app without any limits or aggregations. In BigQuery, every single page view, every click, and every event parameter is stored. Nothing is ever bundled into an "(other)" row. This option requires some technical comfort to query the data, but it guarantees you have access to 100% of your information.

What Does "(not set)" Mean in Google Analytics 4?

So, we've got "(other)" handled. Now, let's talk about its equally annoying cousin, "(not set)." These two are often confused, but they mean very different things.

(other) means GA4 received the data but grouped a long tail of values.
(not set) means GA4 received no data whatsoever for that specific dimension. It’s a literal data gap or a blank space.

Seeing "(not set)" means your tracking is broken or data is missing somewhere in traffic acquisition or from on-site events. The value for a dimension was expected but never arrived.

Common Causes of "(not set)" and How to Fix Them

Troubleshooting "(not set)" involves investigating where the data gap lies. The good news is that it’s almost always fixable.

1. Inconsistent or Missing UTM Tagging

This is the number one reason you’ll see "(not set)" in your traffic acquisition reports (e.g., when viewing dimensions like Session source/medium or Session campaign). It happens when people click a link to your site that doesn’t have the proper URL parameters to tell Google Analytics where they came from.

Scenario: You send out a marketing email, but the links in the email are just plain URLs without UTMs (e.g., www.yoursite.com/sale). Users who click these links will likely show up under Source / Medium as "(not set)".
The Fix: Implement a strict and consistent UTM tagging policy for all your marketing campaigns. Every single URL for emails, social media posts, paid ads (that aren't Google Ads), or QR codes must have UTM parameters. Use Google's Campaign URL Builder to make sure parameters like utm_source, utm_medium, and utm_campaign are always included.

2. Time Lag in Data Processing

Sometimes, data just needs time to get processed and sorted. GA4 data isn’t always real-time, and it can take 24-48 hours for values to be fully attributed. It's possible for some new campaign traffic to temporarily appear as "(not set)" before being correctly assigned.

The Fix: Before you panic, wait a day. Often, if your UTMs are correct, the "(not set)" line item will disappear on its own as data processing completes. Check your reports for "yesterday" instead of "today".

3. Missing Custom Dimension Values on Events

If you set up a custom dimension, such as a "user type" that’s registered when a user logs in (e.g., user_type = Gold_Member), that dimension will only be available for the 'login' & subsequent events during that same session or conversion window (typically 30 days). If you build a report that filters for page_view events, this value may also show as (not set).

The Fix: Review how and when your custom dimensions are set. Set the user property up correctly so its custom dimension is applied to every event in their conversion window for all future sessions. This includes page_view, click, purchase, etc., so it’s always present in your reporting. You can use the Google Tag Manager's Debug preview tool. Let one of your data pros help troubleshoot to see which events have that dimension and which data in that tag is not passed.

4. Consent Mode Configuration Blocking Traffic or Data

Your consent banner may delay data collection. For certain users opting out, tags may fire on a lag, leading to data that appears (not set).

The Fix: This can be remedied by adjusting your Google Tag Manager by configuring “Consent Updated Event Tags.” This will prevent them from firing until consent is granted, allowing first-party cookies to be dropped, or allowing time for a browser session id to append more session-rich information.

Final Thoughts

Understanding the difference between "(other)" and "(not set)" is the key to cleaning up your Google Analytics reports. Remember, "(other)" means GA4 deliberately bundled low-frequency data to save processing power because of cardinality limits, while "(not set)" means the data was never tracked or attributed correctly in the first place, or your banner or tag configuration is misconfigured, causing the issue. By reviewing custom dimension setups and improving your UTM processes, you'll see a whole lot less of both data issues. You’ll be in the driver’s seat of your data and armed to act.

Ultimately, data challenges like these are exactly why we built Graphed. Instead of wrestling with cardinality issues or spending hours building exploration reports—and then having to do the same thing in your other marketing platforms too, like Facebook Ads or HubSpot—Graphed brings everything into one place. We connect directly to tools like Google Analytics in just a few clicks. From there, you just ask questions of your data in plain English. Graphed automates the entire process, letting you build real-time, cross-platform dashboards instantly so you can get the answers you need without having to become a GA4 ninja and professional expert in every advertising platform.