Why you need to preserve your Google Analytics (UA) Historical Data?

Data Analytics

Google Analytics has evolved from Universal Analytics (UA) to Google Analytics 4 (GA4). This major shift has required critical action from business owners worldwide to implement the necessary changes—and the transition is almost complete. 

In July 2023, Universal Analytics stopped processing new data, and by July 1, 2024, access to its historical data will cease.

Without appropriate action, businesses risk losing valuable historical data and insights that inform decision-making and strategy. 

If you haven’t completed your migration from UA to GA4, including preserving UA’s historical data, pay close attention. This article will help you make that final change before saying goodbye to Universal Analytics forever.

UA to GA4: A Brief Timeline of Events

The migration from Universal Analytics to Google Analytics 4 represents a significant shift, necessitating prompt and strategic action. This evolution, critical for maintaining up-to-date website analytics, comes with several key deadlines that underline the urgency for migration and data preservation. 

Here’s a brief timeline highlighting key dates and their implications for your analytics data.

  • ✅ March 2023: Automatic creation of GA4 properties for those who hadn’t migrated, reusing existing site tags where possible.

  • ✅ July 1, 2023: Universal Analytics stopped processing new data. Access to historical data remains until July 1, 2024.

  • January 1, 2024: Notice for Universal Analytics 360 customers about the necessity of migrating to GA4 due to technological and regulatory changes.

  • January 29, 2024: Deprecating specific Universal Analytics features, including Realtime and Lifetime Value reports.

  • ⏱️ Early March 2024: Further deprecation of Universal Analytics features, emphasising the shift towards GA4’s privacy-focused and adaptable framework.

  • ⏱️ March 2024: Recommendation for Universal Analytics 360 users to complete the transition to GA4 and export historical data by this date.

  • ⏱️ July 1, 2024: Universal Analytics properties and API will become inaccessible. Google will delete all data within a week.

What is Historical Data in UA?

Universal Analytics historical data refers to all the collected information about user interactions on a website until UA stopped processing new data in July 2023. 

This data includes everything the platform collected, including user behaviour, traffic sources, conversions, and other metrics that businesses rely on to understand their audience and measure the effectiveness of their marketing efforts.

Unlike previous Google Analytics updates, the GA4 transition doesn’t allow automatic historical data migration. The architecture of GA4 is fundamentally different, focusing on user privacy and a more flexible, event-based data model. This discrepancy means businesses must proactively export and preserve their UA historical data to ensure continuity in their analytics and decision-making processes.

Why Preserving Historical Data Matters

Preserving Universal Analytics historical data is crucial for businesses to maintain a competitive edge while maintaining operational compliance—here’s why.

Trend analysis and improving marketing strategies

Historical data allows for long-term trend analysis, offering insights into user behaviour, traffic patterns, and conversion rates. This analysis is essential for refining marketing strategies, enabling businesses to allocate resources more effectively and tailor their approaches based on past performance. 

Example: Imagine a retail business tracks seasonal customer traffic and sales peaks over several years. This historical data helps the business anticipate demand, manage stock levels efficiently, and plan targeted marketing campaigns for upcoming seasons, ensuring they capitalise on known busy periods.

Potential risks and limitations without historical data

Lacking historical data poses significant risks, including the inability to track progress over time or identify patterns that could inform future decisions. Without this data, businesses may struggle to adapt quickly to market changes, potentially resulting in lost revenue and reduced market share.

Example: A new marketing manager at a tech company can’t access past campaign data from Universal Analytics. They launch a high-budget ad campaign during what they assume is a peak period, not knowing that historical data shows it’s a time of low engagement. The campaign results in lower ROI due to the timing mismatch.

Ensuring data continuity for business and marketing analysis

Data continuity provides a benchmark for measuring current performance against past results, helping to identify areas for improvement and opportunities for growth.

Example: A health and fitness app relies on historical user engagement data to track the effectiveness of new features. By comparing current user engagement levels to those before the feature was introduced, they can quantify its impact on user retention and app usage.

Impact on data-driven decision-making processes

Decision-making becomes more grounded and strategic with historical data to support evidence-based decisions, allowing businesses to rely on analytics rather than assumptions for better outcomes.

Example: A B2B service provider uses historical sales data to identify which services have the highest renewal rates. This insight allows them to focus their sales efforts on promoting their most successful services to new clients, optimising their sales strategy based on proven data.

Legal and compliance implications of data loss

Historical data is critical in regulatory compliance, particularly in the financial and healthcare industries. Losing access to this data could result in non-compliance with legal standards, leading to fines, penalties, and damage to reputation.

Example: A financial institution must retain customer transaction records for a minimum period to comply with anti-money laundering regulations. Losing historical data could mean failing these regulatory requirements, facing legal penalties and losing customer trust.

Step-by-Step Guide to Preserving Google Analytics Historical Data

Follow this step-by-step guide to export and safeguard your UA historical data to ensure its longevity and accessibility to make informed decisions.

Step 1: Audit and prioritise your UA data

Review your Universal Analytics data to identify what’s most valuable. Focus on data that has driven strategic decisions in the past, such as user behaviour reports, conversion data, and traffic sources. 

For example, if your eCommerce site benefits from understanding seasonal purchase patterns, you may want to prioritise:

  • Seasonal sales data: Detailed records of sales transactions during peak seasons, holidays, and promotional periods. This data helps forecast demand and plan inventory.

  • Customer traffic sources: Insights into which channels (organic search, paid ads, social media) drive the most traffic and conversions during different seasons. This information is crucial for allocating marketing budgets effectively.

  • Product performance data: Information on which products sell best at different times of the year, including quantities sold and revenue generated. This data aids in stock management and promotional planning.

  • Conversion rates by season: Analysis of how conversion rates vary across different seasons or specific promotional periods. This data helps optimise the website and marketing messages for higher conversions during peak times.

  • Customer behaviour data: Insights into how customer interactions with the site change with the seasons, including pages visited, time spent on site, and abandonment rates. This information is valuable for improving user experience and reducing bounce rates during high-traffic seasons.

Step 2: Understand GA4’s new features and capabilities

Understanding GA4’s new features and capabilities will significantly enhance historical insights and provide more accurate integration with GA4 data. When you switch to GA4, you’ll have to use a separate platform for historical analysis using UA and GA4 data sets—i.e., spreadsheets, analytics reporting platforms, etc.

GA4 introduces a fundamentally different architecture and data model, focusing more on user engagement, privacy, and cross-platform measurement. By comprehensively understanding these changes, you can map your historical data more effectively into GA4’s framework, ensuring your analysis and reporting continuity.

For example, GA4’s event-based model allows for more granular analysis of user interactions compared to UA’s session-based model. Knowing how to translate your historical session data into meaningful events in GA4 can provide deeper insights into user behaviour. 

Additionally, GA4’s emphasis on user privacy and data collection controls means that knowing how to adjust historical data for analysis while complying with these new standards is vital.

Understanding these differences and how you plan to merge UA and GA4 data will select the most appropriate export method. This step may require testing to determine which solution meets your current and future business needs.

Step 3: Exporting your data

Here are the ways you can export data from Universal Analytics according to Google’s official documentation:

Exporting using different file formats:

  • CSV: Ideal for large datasets and compatibility with most data analysis tools. 

    • Benefit: easy to use with external analysis tools; 

    • Limitation: lacks the formatting and visual appeal of other formats.

  • TSV/TSV for Excel: Best for importing data into spreadsheet applications. 

    • Benefit: straightforward for data manipulation in Excel.

    • Limitation: may require additional formatting steps.

  • Excel (XLSX): Perfect for users familiar with Excel, providing a ready-to-analyse format. 

    • Benefit: integrates seamlessly with Excel’s analytical tools.

    • Limitation: file size can become unwieldy with expansive datasets.

  • Google Sheets: Allows for easy sharing and collaboration online.

    • Benefit: facilitates real-time collaboration. 

    • Limitation: may not support expansive datasets as smoothly as Excel.

  • PDF: Suitable for creating static reports for presentations or archives.

    • Benefit: preserves the layout and formatting.

    • Limitation: not suitable for further analysis.

Exporting might seem like the most straightforward option, but it’s time-consuming with a few significant limitations:

  • Row Limit: Google caps downloads to a maximum of 5,000 rows per report, corresponding to the display limit in the UA interface. If your business has extensive data, you must export multiple times to capture all relevant information for a single report—which may take days, even weeks, to do correctly!

  • Dimension Limit: You can only apply two dimensions per export, limiting the complexity of analysis you can perform directly from these downloads.

  • UA Sampling: Analyses a subset of data to infer trends or patterns in the larger dataset. The main drawback of sampling is its potential to provide an incomplete picture, as reports generated from a subset may not fully represent total data trends.

To maintain 100% data accuracy and integrity, we highly recommend one of the following methods for exporting Universal Analytics data.

Export data to cloud storage or business intelligence platforms:

The Google Analytics Reporting API enables analytics experts to export data directly to cloud storage, linking with Looker Studio or Microsoft Power BI for comprehensive data visualisation. This method supports custom data requests but requires familiarity with API usage and quotas.

Export using the Google Analytics Spreadsheet add-on:

The Google Sheets add-on for Analytics offers a straightforward way to archive data with the added benefit of easy access and sharing capabilities. This method suits users looking for a balance between simplicity and functionality.

Google Analytics 360 export:

Google Analytics 360 customers can export to BigQuery, facilitating complex queries and large-scale data analysis. Exporting using BigQuery is the most comprehensive export method and ensures the best preservation of historical data. It’s also the most technical and requires expert analytics expertise.

Step 4: Setting up and transitioning to Google Analytics 4

Set up your GA4 property through the Google Analytics admin panel, ensuring you configure data streams to match your previous UA setup as closely as possible. 

For example, if your UA account tracked multiple websites, set up corresponding GA4 data streams. This step ensures continuity in data collection, albeit with the challenge of adapting to GA4’s different data models and metrics.

Recommended reading for this step:

Step 5: Validating your GA4 setup

After transitioning, closely monitor your GA4 data against your UA data to validate the setup’s accuracy. Look for discrepancies in user counts, session data, and event tracking. Implementing consistent UTM parameters can help ensure tracking continuity. 

This validation is critical for confirming the reliability of your new GA4 setup, though it may highlight areas requiring adjustment or further configuration.

Best Practices for Data Storage and Preservation

Once you export universal analytics data, storage and ongoing preservation are crucial to ensure long-term accessibility and security.

Options for storing UA data after exporting

Here are some common methods for storing your historical UA data:

Cloud storage services platforms like Google Drive, Dropbox, or AWS S3 offer scalable, flexible storage solutions with remote access.

  • Best suited for: Businesses of all sizes seeking scalability and remote access.

  • Pros:

    • Scalable storage options to accommodate growing data needs.

    • Enhanced accessibility with data available from any location.

  • Cons:

    • Dependence on internet connectivity for access.

    • Potential concerns over data sovereignty and privacy, depending on the provider.

On-premise servers offer control over security measures for sensitive data, though they require maintenance and physical space.

  • Best suited for: Larger enterprises or businesses with specific regulatory compliance requirements.

  • Pros:

    • Complete control over data security measures.

    • No reliance on third-party services for data access.

  • Cons:

    • Requires significant upfront investment and ongoing maintenance costs.

    • Server hardware requires physical space.

External hard drives provide a physical backup, offering portability and control over access.

  • Best suited for: Small businesses or individual departments within a larger organisation looking for a simple, cost-effective backup solution.

  • Pros:

    • Portable and easy to use for offsite backups.

    • One-time cost without ongoing subscription fees.

  • Cons:

    • Risk of physical damage or loss.

    • Limited storage capacity compared to other solutions, requiring frequent replacements or additional purchases as data grows.

Long-term data accessibility and security considerations

  • Regular access reviews: Ensure only authorised personnel have access to the data, reviewing permissions regularly to maintain security.

  • Encryption: Encrypt data both in transit and at rest to protect against unauthorised access.

  • Compliance with data protection laws: To ensure legal compliance, adhere to GDPR, CCPA, or other relevant data protection regulations.

Tips for organising and cataloguing historical data

  • Structured filing system: Create a clear, hierarchical structure for files and folders based on categories like date, data type, or business function.

  • Metadata tagging: Use metadata tags for easier searching and retrieval of data, including details such as date range, content type, and relevance.

  • Regular audits: Conduct routine audits of your data storage to remove redundancies and ensure the system remains efficient and relevant.

Strategies for data backup and protection to mitigate data loss risks

  • Regular backups: Implement a schedule for regular backups to multiple locations, ensuring you have up-to-date copies in case of data loss.

  • Data recovery plan: Have a clear, documented plan for data recovery in the event of loss, including steps to restore data and minimise downtime.

  • Use of RAID Systems: For on-premise storage, RAID systems can provide redundancy, helping to prevent data loss in case of a hardware failure.

By adhering to these best practices, you can ensure that your valuable historical data from Universal Analytics remains secure, accessible, and organised, supporting your business’s analytical needs well into the future.

Preserve Your UA Data With Support From Trusted Data Analytics Experts

With the July deadline fast approaching, migrating from Universal Analytics to Google Analytics 4 is not just necessary; it’s urgent. Preserving historical data and adapting to GA4’s advanced platform requires careful planning and execution.

Our data analytics experts have helped many companies transition from UA to GA4 since Google introduced Google Analytics 4 in October 2020. Book a call with a Metric Labs data expert to help design the right data solution for your business.



Like this blog post?

Sign up to our email newsletter – Lab Report – and never miss a new one!

How mature is your Google Analytics 4 set up?