How to Archive Your Universal Analytics Historical Data

Table of Contents

  1. Introduction
  2. Phase 1: Making a Plan
  3. Phase 2: Choosing an Archiving Method
  4. Phase 3: Ensuring Comprehensive Data Capture
  5. Conclusion
  6. FAQ

Introduction

The countdown to July 1, 2024, has begun, and it marks a significant deadline for anyone using Google’s Universal Analytics. On this date, historical data from Universal Analytics properties will be deleted, impacting all users, including Analytics 360 customers. If you haven’t yet taken steps to archive this invaluable data, the time to act is now. This blog post will guide you through the essential phases and methods to properly archive your Universal Analytics data, ensuring you have access to historical insights even after the deadline. By the end of this post, you'll be equipped with the knowledge to identify your key data, select the best archiving method, and ensure your data retention efforts are thorough and effective.

Phase 1: Making a Plan

The foundation of any successful archiving process is a comprehensive plan. Here are the key elements you need to address:

  1. Identify Important Data: Pinpoint what specific data is critical for your future analysis. This might include traffic reports, conversion metrics, and user behavior statistics.

  2. Determine Data Retention Period: Decide the longevity for which you need to retain your data. Do you need a decade's worth of insights, or will a few years suffice?

  3. Define Review Cadence: Establish how frequently you need to review and use this data. A clear schedule can guide the archiving process and ensure relevance in your analyses.

Having a solid plan will streamline the archival process and help you avoid last-minute scrambles to save your data.

Phase 2: Choosing an Archiving Method

There are four primary methods for archiving your Universal Analytics data, each with distinct advantages and limitations:

Option 1: Manual File Downloads

This method is straightforward but labor-intensive. It involves manually exporting data from each report in Universal Analytics and saving it to Google Sheets, Excel, or CSV files.

  • Steps:

    • Open the desired report in Universal Analytics
    • Set the date range, dimensions, and metrics
    • Change the number of rows from the default to the maximum (5,000)
    • Export data to Google Sheets, Excel, or CSV

  • Pros: Simple and does not require technical expertise

  • Cons: Extremely time-consuming, especially for large datasets

Option 2: Google Sheets Add-On

The Google Analytics add-on for Google Sheets simplifies the archiving process significantly. This tool uses the Google Analytics API to download data into Google Sheets without requiring extensive technical knowledge.

  • Steps:

    • Create a new Google Sheet and install the Google Analytics add-on
    • Build a report using the add-on’s interface, adjusting configurations as needed
    • Use the Dimensions and Metrics Explorer for accurate API codes

  • Pros: Easier for non-technical users, supports formulas for more refined data pulls

  • Cons: May encounter sampling issues with large or complex datasets, and has a cell limit of 10,000,000 per sheet

Option 3: Google Analytics API

For those with web development resources, using the Google Analytics API directly can be a powerful method to archive data. This approach provides greater customization and can handle larger datasets with fewer limitations.

  • Steps:

    • Access the Google Analytics API documentation
    • Develop a script to pull the required data according to your archiving plan

  • Pros: Highly customizable, less prone to limitations faced by Google Sheets

  • Cons: Requires programming skills and can be complex to implement

Option 4: BigQuery

BigQuery offers a superior method for data archiving, especially for Analytics 360 users. It provides a data warehouse where you can store, query, and analyze vast datasets efficiently.

  • Steps for Analytics 360 Users:

    • Utilize Google’s native export feature to transfer data to BigQuery
    • Execute the transfer and schedule periodic updates if needed

  • Steps for Non-Analytics 360 Users:

    • Manually export data from Universal Analytics
    • Load the exported data files into BigQuery

  • Pros: Ideal for large datasets, fast queries via SQL, robust storage solution

  • Cons: Non-360 users have a more complicated export process

Phase 3: Ensuring Comprehensive Data Capture

Before you declare your archiving project complete, it’s crucial to verify that all planned data has been captured:

  1. Double-Check Data: Review all exported or archived data against your initial plan to ensure no critical information was overlooked.

  2. Test Data Accessibility: Ensure that the archived data can be easily accessed and utilized for future analysis.

  3. Backup Data: Create multiple backups of your archived data in different formats or locations to prevent any potential data loss.

Conclusion

Archiving your Universal Analytics historical data requires a methodical approach to ensure completeness and usability. By making a thorough plan, choosing the appropriate method, and verifying your data capture, you can safeguard invaluable insights from disappearing post-July 1, 2024. Whether you opt for manual downloads, the Google Sheets add-on, the Google Analytics API, or BigQuery, each method offers a pathway to preserving your data. Prioritize this process to maintain continuity in your data analysis efforts.

FAQ

Why is archiving Universal Analytics data important?

Data from Universal Analytics provides critical historical insights that can inform future business and marketing strategies. Without archiving, these valuable data points will be lost after July 1, 2024.

Which archiving method should I choose?

It depends on your team's technical skills and the volume of data. Manual file downloads are simple but time-consuming, while the Google Sheets add-on and API offer more streamlined solutions. BigQuery is recommended for large-scale data archiving.

How can I avoid data sampling issues when using Google Sheets?

Limit the amount of data pulled per request, split the data into smaller time frames, or reduce the granularity of the data to mitigate sampling issues.

Can I automate the data archiving process?

Yes, using the Google Analytics API or BigQuery with scheduled data transfers can help automate the archiving process, particularly for large datasets.

What happens if I miss the July 1 deadline?

After July 1, 2024, you will not be able to access any Universal Analytics data, resulting in the loss of all historical data not archived beforehand.

By following this comprehensive guide, you can ensure the historical data from your Universal Analytics account is preserved and accessible for future use. Start archiving today before time runs out.