Just as you’ve finally settled into the shift from Universal Analytics (UA) to Google Analytics 4 (GA4) and started to get a handle on its new metrics, Google has yet another deadline for organizations to meet. On July 1, 2024, Google will permanently remove access to all Universal Analytics data. Organizations need to act before that deadline if they want to preserve their historical data. In this blog post, we’ll explore how to determine what information to retain and the best way to store it. Additionally, we’ll cover GA4 retention considerations that may impact your decision. We’ll also provide insights into the reasons behind this transition, its challenges, and practical strategies for ensuring the continuity of your analytics efforts.

Breaking down the loss of access to UA data and GA4 data retention

Universal Analytics (UA, sometimes called GA3) was the previous version of Google Analytics, with GA4 introduced as the latest version in 2022. With the GA4 rollout, standard UA properties ceased collecting new data on July 1, 2023. All existing UA data remains accessible, but organizations will lose access to that information on July 1, 2024.

Meanwhile, GA4 has new data retention limits that didn’t exist in UA. With UA, organizations could retain user-level and event-level data indefinitely, but in GA4, the maximum data retention period for user-level and event-level data is 50 months, and that’s only available to users of Google’s paid Analytics 360 plan. Organizations using the free version of GA4 can only retain this data for 14 months.

We mention these two considerations together because we often find that an organization’s answer to whether it will retain its GA4 data beyond 14 months significantly impacts whether and how it should retain its UA data.

To reiterate, here are answers to a few common questions before we outline the options available:

Can I access my old UA data after July 1, 2024?

After this date, UA data will no longer be available unless you’ve saved or migrated it.

Can I migrate data or reports from UA to GA4?

GA4 is a replacement for UA rather than an update to it. As a result, you cannot migrate UA data to GA4. One reason for this is the difference in the tool’s data models: UA relies on session-based data, while GA4 operates on an event-based model.

Is there a way to export UA or GA4 data?

Exporting data from Google Analytics is possible and recommended by Google as the solution for long-term data retention. By exporting data, users can maintain access to historical comparisons and enable future analysis.

5 options for exporting UA and GA4 data

Given the time constraints on access to UA data, it’s imperative to determine your organization’s approach promptly. This guide will assist you in exploring the available options. We’ll delve into five main approaches to exporting data, allowing you to make an informed decision tailored to your needs.

1. Export Google Analytics 360 and GA4 data to BigQuery

Users of Google’s paid 360 version of UA have the valuable option to export their UA data directly into Google BigQuery, a powerful data warehouse solution. In GA4, Google has extended this ability to standard (free) properties. However, the standard properties are limited to exporting a maximum of 1 million events per day. This integration is ideal for organizations looking to analyze large volumes of data beyond the capabilities of standard reporting interfaces.

One advantage of moving your analytics data into BigQuery is data from other sources, such as your CMS, CRM, or social media management software, can also be added, enabling a more comprehensive view of information about your audiences.

Working with Google Analytics data in BigQuery requires writing queries using SQL-like syntax. You should ensure that your staff is comfortable working with the raw data through querying, as various data sources (such as GA4 and UA) will have differing data models and require manipulation before use. Additionally, while BigQuery is compatible with many other systems, confirming that your organization’s other data tools will work smoothly with it is worthwhile.

2. Use a third-party data connector

Many third-party tools are available that allow you to export data from UA or GA4 and import it to the data warehouse or database of your choice. Some popular tools include Supermetrics, FiveTran, Analytics Canvas, and Power My Analytics. The best option for your organization will depend on your existing data tools, budget, and interface preferences.

Data connectors can consolidate data from many different platforms, not just UA and GA4, significantly streamlining the data aggregation process and providing a more comprehensive view of your organization’s data. They can also provide a platform for ETL processes: in other words, Extracting data from the systems where it is collected, Transforming, cleaning, or otherwise manipulating it into a more usable structure, and then Loading it directly to business intelligence tools or data warehouses including BigQuery. Connectors eliminate the need for manual imports and reduce the risk of manual errors. Additionally, automation enables them to update your data more frequently.

Third-party connectors usually offer low-code options for ETL processes, but there may still be some need for SQL querying or other technical skills and understanding. While these tools can provide significant benefits in terms of efficiency and data management, there may be challenges in integrating and utilizing these tools within an existing data system. Their costs can vary as well, and they have a variety of pricing models that may or may not work well for your organization. Architects and analysts may need to invest time in understanding the intricacies of the tools available to make an informed decision.

3. Export data using Google Analytics Dev Tools: Query Explorer (API)

Google Analytics operates on a query-based framework, allowing users to use Core Reporting APIs to extract data from a Google Analytics view and generate customized results.

How to Use Query Explorer

Query Explorer is specifically designed for UA data, though you can also use it for GA4 data. Here’s a step-by-step guide on how to effectively utilize this powerful resource:

1. Open the Google Analytics Query Explorer at ga-dev-tools.google/query-explorer/.
2. Select Your Account and Property: Log in to your Google account and choose the specific account, property, and view you want to work with.
3. Set Query Parameters: The “Set query parameters” section is where most of your selections will occur:
   • Date Range: Choose the timeframe for your query using formats such as calendar dates (e.g., 2021-01-31), relative dates (e.g., today or yesterday), or specific time intervals (e.g., 30daysAgo or 7daysAgo).
   • Metrics: Select quantitative measurements like Pageviews, New Users, or Conversions.
   • Dimensions: Choose qualitative attributes like Source/Medium, Country or Region, or Device Category to provide context to your metrics.
   • Filters (optional): Narrow down your dataset to focus on specific subsets of information.
   • Segments (optional): Isolate specific groups of users or sessions within your data for more targeted analysis.
4. Run Query: Once your parameters are set, click the “Run Query” button. Query Explorer will generate a report of your specific data and display it at the bottom of the page.
5. Export: Export your report as a TSV file to store your data outside of Google Analytics and Query Explorer, enabling it to be imported or analyzed in the future. Alternatively, copy the API Request URL to integrate the data into other applications.
6. Repeat: Follow the same process to archive all the metrics and dimensions you may want for future reference.

This method allows you to extract highly customized reports from a straightforward interface. There are automated and manual export options, and the resulting data can be used in various ways. However, the API that Query Explorer relies on is subject to quota limits, so this approach works best for small datasets. Organizations with large datasets can explore scheduling data extraction over time to overcome quota limitations.

4. Export data using the Google Analytics Spreadsheet Add-on

The Google Analytics Spreadsheet Add-on is a tool developed by Google that allows users to import data from their UA properties directly into Google Sheets. This add-on provides a seamless way to access and analyze Google Analytics data within the familiar environment of Google Sheets, enabling users to create custom reports, perform data analysis, and visualize insights using the powerful features of Google Sheets.

This add-on is based on the same API as the Query Explorer described in the previous section, so it has very similar benefits and drawbacks. The primary difference is that the results will be held in a Google Sheet, allowing for further data manipulation and reporting directly in Google Sheets.

Additionally, Google has released a new version of this add-on for GA4 called Reports Builder for Google Analytics. However, the new version is less capable, and many users have reported challenges with the new add-on.

5. Manual Export

The final option is manually exporting any data you need from Google Analytics. This approach is the most straightforward way to retain your historical data, but the process can be tedious.

How to Manually Export Data from UA

1. Open your standard Google Analytics report, such as Acquisitions > All Traffic > Sources/Mediums.
2. Customize the report as needed by applying segmentation or filtering. For example, you could segment your Sources/Mediums report by country or device.
3. Once the report is customized, click on the “EXPORT” option located below the report.
4. From the export options, choose the desired format for the document from the drop-down list. Select the format that best suits your needs and preferences.

While this method offers the most straightforward way to export historical data from Google Analytics, it may also have the most limitations for most use cases. For example, large datasets may encounter restrictions during the export process, potentially affecting the completeness of the exported historical data. It can also be time-consuming and requires determining a storage location and naming convention for the resulting files.

Which option for exporting Google Analytics data is best?

The best option for exporting Google Analytics data depends on your needs and preferences. We strongly encourage you to consider your future reporting needs, which will define your export and storage requirements. Once you know your requirements, here are some factors to consider when choosing between your options:

1. Format: Consider the format that best suits your needs. Google Analytics offers various export formats, including PDF, spreadsheet, and CSV, or export to data warehouses such as BigQuery. Choose the format that allows easy analysis and integration with your existing tools and workflows.
2. Customization: Evaluate the level of customization offered by each export option. Some options allow you to customize the data before exporting by applying filters or selecting specific metrics and dimensions.
3. Automation: Consider the skills and time available to your data team. More automated processes can save significant time but may require specialized skills to work with the tools and the resulting data sets.
4. Data size: Review the size of the data you need to export. Some export options may limit the amount of data that can be exported at once. Ensure that the chosen option can handle the volume of data you need to export without encountering restrictions.
5. Compatibility: Evaluate the export option’s compatibility with your existing tools and systems. Choose an option that seamlessly integrates with your preferred analytics or data management platforms to ensure smooth data transfer and analysis.

Ultimately, the best option for exporting Google Analytics data is the one that aligns with your specific requirements, offering the right balance of format, customization, automation, data size handling, and compatibility.

Work with Forum One

It can be overwhelming and worrying to face the possibility of losing the data you have in UA, especially while you may be simultaneously learning to use GA4 and understanding its data storage limitations. We hope this blog post clarifies your options and provides a solution that works for you and your organization’s unique needs. Forum One is here for you if you still have questions or need help! Work with our data and analytics experts to determine the right solution for you and complete the migration efficiently and accurately.