How to Archive Your Universal Analytics Data

Time is running out to save your historical Universal Analytics data. Follow these steps to archive your historical data before the 1st of July deadline.

We've known about this deadline for quite some time now, but we're now counting down the days until you'll no longer be able to retrieve your historical data from Google's Universal Analytics. Here are some simple steps to follow to ensure you don't lose your data as you transition over to Google Analytics 4 (GA4).

Phase 1: Plan Your Archive

Before starting the archiving process, it's important to create a clear plan:

• Identify Crucial Data: What specific data points are most valuable to your organisation? Prioritise downloading data you regularly use, such as conversion and sales figures. 
• Data Retention Period: How many years of data do you require? Many businesses have been using Google Analytics since the mid-2000s. Does your organisation need data stretching back nearly two decades? Determine a cut-off point for archiving data. As a minimum recommendation, consider archiving data back to around 2018 to capture pre-pandemic trends, as the pandemic significantly impacted data for many companies.
• Data Review Frequency: How often do you typically review your data? Is it weekly or monthly? Depending on the archiving method chosen in Phase 2, you may need to organise data into specific timeframes.
  

Phase 2: Choose an Archiving Method

There are four primary options available for archiving your Universal Analytics data. Each method has its own advantages and disadvantages, so pick the one that best suits your team's capabilities and resources.

• Option 1: Manual File Downloads

  • Pros: Easy to use for most users, free of charge.
  • Cons: Time-consuming, cumbersome to access data later for reporting, limited to 5,000 rows.

  This is the simplest method, but it's also the most time-consuming. Following your archive plan for years, data points, and frequency, you'll need to access each report within the Google Universal Analytics interface. Set the date range, dimension, and metric settings as required.

  Remember to adjust the number of rows from the default 10 to the maximum 5,000 to capture as much data as possible. Click the export button and choose to export the data to a Google Sheet, Excel spreadsheet, or CSV file. Repeat this process until you've downloaded all the data identified in your archive plan.

• Option 2: Downloading Data to Google Sheets Using the Google Analytics Add-on (Recommended for Non-Technical Users)

  • Pros: Relatively straightforward for users with spreadsheet experience, free, downloads data quickly.
  • Cons: Restricted to a set timeframe (e.g. monthly), limitations on data per sheet, can encounter sampling issues.

  This option is fairly simple for most users to implement. Create a new Google Sheet and install the Google Analytics add-on. The add-on essentially uses the Google Analytics API to download data directly to Google Sheets.

  The first time you use the add-on, you'll build a report using its interface. However, after running the first report, you can simply update the "Report Configuration" tab and create additional reports directly in columns of that sheet. You can also use formulas within the "Report Configuration" sheet. Utilise the "Dimensions and Metrics Explorer" to find the appropriate API code to enter into each field.

  One drawback of the Google Sheets method is that you may encounter sampling if you attempt to pull too much data at once or if your report is overly detailed. You can find the sampling level on the report's data tab in cell B6. If your report contains sampled data, consider reducing the amount of data you pull. For example, you could split the data pull into two separate timeframes.

  However, if avoiding sampling is impossible, check the data sample percentage on the report. Then, on the "Report Configuration" tab, unhide rows 14-17 and adjust the sampling size on row 15 to match this level, ensuring data consistency.

  Tip: The add-on defaults to 1,000 lines of data in a report. Simply delete the 1,000 under the line labelled "Limit" (typically row 11).

  Another drawback of the Google Sheets option is that each file is limited to 10,000,000 cells. Typically, each sheet starts with 26 columns (A to Z) and 1,000 default rows (or 26,000 cells). If your downloaded data exceeds the 10,000,000 cell limitation (which is very likely to happen) then you may need to have multiple Google Sheets to download all of the data.

• Option 3: Download data using the Google Analytics API
  
  
  • Pros: Pulls data quickly once it is set up

  • Cons: Requires web development, doesn't solve the data sampling issue.

If you have web development resources that can work on the archiving project, they can pull the data detailed in your plan using the Google Analytics API directly. This works similarly to the aforementioned Google Sheets add-on option, but it’s a more manual process in programming the API calls.

• Option 4: Download data to BigQuery (best option overall)

  Pros: Simple to access data later for reporting, increased data insights, most flexible for data.

  Cons: Complicated for novices to set up initially, can involve fees for BiqQuery, may require technical resources to set up, need to involve an additional tool.

  The main benefit of archiving your Universal Analytics data to BigQuery is that BigQuery is a data warehouse that allows you to ask questions of the data set through SQL queries to get your data very quickly. This is especially useful in accessing this data for reporting later. 

Phase 3: Ensure you’ve captured it all

Before you consider the project complete, be sure to double-check your archived data to ensure you’ve captured everything you planned to archive.

On July 1st 2024, you will no longer be able to access Universal Analytics data, either by API or through the interface. 

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