{"id":7641,"date":"2024-05-20T15:48:41","date_gmt":"2024-05-20T15:48:41","guid":{"rendered":"https:\/\/tup.kxe.temporary.site\/how-to-archive-your-universal-analytics-historical-data\/"},"modified":"2024-05-20T15:48:41","modified_gmt":"2024-05-20T15:48:41","slug":"how-to-archive-your-universal-analytics-historical-data","status":"publish","type":"post","link":"https:\/\/okdesign.ca\/en\/how-to-archive-your-universal-analytics-historical-data\/","title":{"rendered":"How to archive your Universal Analytics historical data"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p>Another <a rel=\"nofollow\" href=\"https:\/\/martech.org\/topic\/marketing-analytics\/google-analytics-4\/\">Google Analytics 4<\/a> migration project deadline is fast approaching, and this deadline is hard set. On July 1, Google will <a rel=\"nofollow\" href=\"https:\/\/martech.org\/reminder-google-is-turning-off-all-universal-analytics-services-and-apis\/\">delete all historical data<\/a> from Universal Analytics properties. This deadline also affects Analytics 360 customers.<\/p>\n<p>With little more than one month until the deadline, if you have not done so by now, your organization needs to prioritize archiving your historical data. There are three main phases I recommend for approaching this project.<\/p>\n<h2 class=\"wp-block-heading\" id=\"h-phase-1-make-a-plan\">Phase 1: Make a plan<\/h2>\n<p>Before archiving data, it\u2019s important to decide:<\/p>\n<p><!-- \/1038259\/MT_Post-text --><\/p>\n<h3 class=\"wp-block-heading\" id=\"h-what-specific-data-is-important-to-you-nbsp\">What specific data is important to you?\u00a0<\/h3>\n<ul>\n<li>Prioritize downloading data that you regularly refer to, such as conversion and sales data.<\/li>\n<li>Make a full list of the data you need to archive.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\" id=\"h-how-many-years-of-data-do-you-want-to-keep\">How many years of data do you want to keep?<\/h3>\n<ul>\n<li>Many of us have been using Google Analytics since the mid-2000s \u2013 does your organization need to archive data from nearly 20 years ago?\u00a0<\/li>\n<li>Decide how far back you want to archive data. I recommend, at minimum, to consider archiving back to 2018 or so to ensure you have pre-pandemic data since the pandemic really presented data anomalies for many companies.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\" id=\"h-at-what-cadence-do-you-review-data\">At what cadence do you review data?<\/h3>\n<ul>\n<li>Consider how often you typically report on your data. Is it weekly? Monthly?\u00a0<\/li>\n<li>Depending on the archiving method you choose in Phase 2, you may need to organize the data into specific time increments.<\/li>\n<\/ul>\n<p><strong><em>Dig deeper: <a rel=\"nofollow\" href=\"https:\/\/martech.org\/why-the-shift-from-conversions-to-key-events-in-ga4-is-a-game-changer\/\">Why the shift from \u2018conversions\u2019 to \u2018key events\u2019 in GA4 is a game-changer<\/a><\/em><\/strong><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-phase-2-choose-an-archiving-method\">Phase 2: Choose an archiving method<\/h2>\n<p>There are four main options available for archiving your Universal Analytics data. Each has its own pros and cons, so choose a method based on your team\u2019s resources and skills.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-option-1-manual-file-downloads\">Option 1: Manual file downloads<\/h3>\n<ul>\n<li><strong>Pros<\/strong>: Easy for almost all users to do, free.<\/li>\n<li><strong>Cons<\/strong>: Time-consuming, cumbersome, difficult to access data for reporting later, limited to 5000 rows.<\/li>\n<\/ul>\n<p>While this is the easiest process to understand, it is also time consuming. <\/p>\n<p>Following your plan for years, cadence and data points, you\u2019ll need to go into each report in the Google Universal Analytics interface, set the date, dimension and metric settings as needed. <\/p>\n<p>Also, remember to change the number of rows from the default of 10 to the maximum of 5,000 rows to ensure you capture as much data as possible. <\/p>\n<p>Click the export button and export data to a Google Sheet, Excel or CSV. Repeat this process until you have downloaded all of the data identified in your archive plan.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-option-2-download-data-to-google-sheets-using-the-google-analytics-add-on-best-option-for-tech-novices\">Option 2: Download data to Google Sheets using the Google Analytics add-on (best option for tech novices)<\/h3>\n<ul>\n<li><strong>Pros<\/strong>: Fairly simple to implement for most users with spreadsheet experience, free, fast to download.<\/li>\n<li><strong>Cons<\/strong>: Restrictive to a set timeframe (e.g., monthly), each sheet has total data limitations, often encounters sampling issues.<\/li>\n<\/ul>\n<p>This option is fairly simple for most users to perform. Create a new Google Sheet and add the <a rel=\"nofollow noopener\" href=\"https:\/\/workspace.google.com\/marketplace\/app\/google_analytics\/477988381226\" target=\"_blank\">Google Analytics spreadsheet add-on<\/a>.<\/p>\n<p>The add-on essentially uses the Google Analytics API to download data to Google Sheets but doesn\u2019t require API programming knowledge to operate. Google has compiled a basic overview of this approach in this <a rel=\"nofollow noopener\" href=\"https:\/\/support.google.com\/analytics\/answer\/13851096\" target=\"_blank\">help document<\/a>.<\/p>\n<p>The first time you use the add-on, you\u2019ll build a report using the add-on\u2019s interface. But after the first report is run, you can also simply update the Report Configuration tab and create additional reports directly in columns of that sheet.<\/p>\n<p>You can also conveniently use formulas in the Report Configuration sheet. Use the <a rel=\"nofollow noopener\" href=\"https:\/\/ga-dev-tools.google\/dimensions-metrics-explorer\/\" target=\"_blank\">Dimensions and Metrics Explorer<\/a> to find the proper API code to enter into each field.\u00a0<\/p>\n<p>One drawback of the Google Sheets method is that you may encounter sampling if you pull too much data at once (e.g., your entire 20-year dataset for sessions) or your report is too detailed (too many dimensions pulled together for a high level of granularity).\u00a0<\/p>\n<p>When you run a report, you\u2019ll see the sampling level on the report\u2019s data tab in cell B6. If your report contains sampled data, you may want to consider reducing the amount of data in this particular pull, for example, you might split the pull into two time frames. <\/p>\n<p>However, if you just can\u2019t avoid sampling, check the data sample percentage on the report. Then, on the Report Configuration tab, unhide rows 14-17 and the sampling size on row 15 to this level so that your data remains consistent.<\/p>\n<p><strong>Tip<\/strong>: The add-on defaults to 1,000 lines of data in a report. Simply delete the 1,000 under the line labeled \u201cLimit\u201d (typically row 11).<\/p>\n<p>Another drawback of the Google Sheets option is that each file is limited to 10,000,000 cells. Typically, each sheet starts out with 26 columns (A to Z) and 1,000 default rows (or 26,000 cells).<\/p>\n<p>If your downloaded data exceeds the 10,000,000 cell limitation (which can very likely happen), then you may need to have multiple Google Sheets to download all of the data.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-option-3-download-data-using-the-google-analytics-api\">Option 3: Download data using the Google Analytics API<\/h3>\n<ul>\n<li><strong>Pros<\/strong>: Pulls data quickly once set up.<\/li>\n<li><strong>Cons<\/strong>: Requires web development knowledge and resources, doesn\u2019t solve the data sampling issue, API quota limitations.<\/li>\n<\/ul>\n<p>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.\u00a0<\/p>\n<p>This works similarly to the aforementioned Google Sheets add-on option, but it\u2019s a more manual process in programming the API calls.<\/p>\n<p>To learn about how to use the API for this project, visit <a rel=\"nofollow noopener\" href=\"https:\/\/support.google.com\/analytics\/answer\/11583528?hl=en#export\" target=\"_blank\">Google\u2019s archiving information page<\/a> and review the second bullet, which details several resources and considerations for using the API for this data export project.<\/p>\n<h3 class=\"wp-block-heading\" id=\"h-option-4-download-data-to-bigquery-best-option-overall\">Option 4: Download data to BigQuery (best option overall)<\/h3>\n<ul>\n<li><strong>Pros<\/strong>: Simple to access data later for reporting, increased data insights, most flexible for data.<\/li>\n<li><strong>Cons<\/strong>: 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.<\/li>\n<\/ul>\n<p>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.\u00a0<\/p>\n<p><strong>Analytics 360 users<\/strong><\/p>\n<p>If you are an Analytics 360 user, Google provides a native export to BigQuery. I recommend this method. <a rel=\"nofollow noopener\" href=\"https:\/\/support.google.com\/analytics\/answer\/3416092\" target=\"_blank\">See these instructions from Google<\/a>.<\/p>\n<p><strong>Everyone else<\/strong><\/p>\n<p>If you\u2019re not an Analytics 360 user, then you\u2019ll need to approach the BigQuery backup differently because Google <strong>does not<\/strong> provide innate BigQuery backup options in Universal Analytics for non-360 users.\u00a0<\/p>\n<p>Here are the steps you\u2019ll want to follow:<\/p>\n<ul>\n<li><strong>Step 1: <\/strong>Create a Google API Console project and enable BigQuery.\n<ul>\n<li>Log in to the <a rel=\"nofollow noopener\" href=\"https:\/\/console.cloud.google.com\/\" target=\"_blank\">Google APIs Console<\/a>.<\/li>\n<li>Create a Google APIs Console project.<\/li>\n<li>Navigate to the APIs table.<\/li>\n<li>Activate BigQuery.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Step 2: <\/strong>Prepare your project for BigQuery export.\n<ul>\n<li>Ensure Billing is enabled for your project. You may not need to pay anything, but it will vary depending on the usage and data you have.<\/li>\n<li>If prompted, create a billing account.<\/li>\n<li>Accept the free trial if it\u2019s available.<\/li>\n<li>Validate Billing enablement. Open your project at <a rel=\"nofollow noopener\" href=\"https:\/\/console.cloud.google.com\/bigquery\" target=\"_blank\">https:\/\/console.cloud.google.com\/bigquery<\/a>, and try to create a data set in the project. Click the blue arrow next to the project name, then click <em>Create data set<\/em>. If you can create the data set, billing is setup correctly. If there are any errors, make sure billing is enabled.<\/li>\n<li>Add the service account to your project. Add <a rel=\"nofollow\" href=\"https:\/\/martech.org\/cdn-cgi\/l\/email-protection\" class=\"__cf_email__\" data-cfemail=\"a3c2cdc2cfdad7cac0d08ed3d1ccc0c6d0d0cacdc48ec7c6d5e3d0dad0d7c6ce8dc4d0c6d1d5cac0c6c2c0c0ccd6cdd78dc0ccce\">[email\u00a0protected]<\/a> as a member of the project, and ensure that permission at the project level is set to Editor (as opposed to BigQuery Data Editor). The Editor role is required in order to export data from Analytics to BigQuery.<\/li>\n<li>If you are in the EU, please also <a rel=\"nofollow noopener\" href=\"https:\/\/support.google.com\/analytics\/answer\/3416092#step2.1&amp;zippy=%2Cin-this-article\" target=\"_blank\">review additional requirements<\/a>.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Step 3: <\/strong>Set up a free trial of Supermetrics. Similar to the Google Sheets add-on in option 2 above, Supermetrics is a tool that helps non-technical users interface with and use APIs. They offer a free 14-day trial, which is likely all you\u2019ll need for this project since you\u2019re only downloading the Universal Analytics data once (not regularly).\n<ul>\n<li>Connect the BigQuery data source in the Supermetrics dashboard.\u00a0<\/li>\n<\/ul>\n<\/li>\n<li><strong>Step 4: <\/strong>In BigQuery, establish the connection to Supermetrics.\n<ul>\n<li>Navigate to BigQuery, then to <em>Data transfers<\/em>.<\/li>\n<li>Click <em>+ Create transfer.<\/em><\/li>\n<li>Select your <em>Google Analytics by Supermetrics<\/em> as your source and click <em>Enroll<\/em>.\u00a0<\/li>\n<li>Fill in the transfer details. <a rel=\"nofollow noopener\" href=\"https:\/\/support.supermetrics.com\/support\/solutions\/articles\/19000098531-how-to-configure-a-data-transfer-in-bigquery\" target=\"_blank\">See detailed instructions on how to set up a transfer<\/a>.<\/li>\n<li>Under<em> Third-party connection<\/em>, click <em>Connect source<\/em>.<\/li>\n<li>Accept the agreement.<\/li>\n<li>Click <em>Authorize with your Google data source<\/em>.<\/li>\n<li>Click<em> Sign in with Google<\/em>.<\/li>\n<li>Sign in with the Google Account you use with this data source. This doesn\u2019t have to be the same as the Google Account you use with Supermetrics.<\/li>\n<li>Click <em>Allow<\/em>.<\/li>\n<li>Select the accounts you\u2019d like to include in your reporting and define the transfer settings.<\/li>\n<li>Click <em>Submit<\/em>.<\/li>\n<li>Click <em>Save<\/em>.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Because you only need to transfer the Universal Analytics data one time, you can also change the schedule on the transfer to <em>On demand<\/em> and then run the transfer now.\u00a0<\/p>\n<p><strong><em>Dig deeper: <a rel=\"nofollow\" href=\"https:\/\/martech.org\/3-ways-ga4-is-way-better-than-ua\/\">3 ways GA4 is way better than UA<\/a><\/em><\/strong><\/p>\n<h2 class=\"wp-block-heading\" id=\"h-phase-3-ensure-you-ve-captured-it-all\">Phase 3: Ensure you\u2019ve captured it all<\/h2>\n<p>Before you consider the project complete, be sure to double-check your archived data to ensure you\u2019ve captured everything you planned to archive.<\/p>\n<hr\/>\n<hr\/>\n<p>On July 1, you will no longer be able to access Universal Analytics data, either by API or through the interface.\u00a0<\/p>\n<p><!-- START INLINE FORM --><\/p>\n<p><!-- END INLINE FORM -->\n<\/div>\n<p><em>Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed <a rel=\"nofollow\" href=\"https:\/\/martech.org\/staff\/\">here<\/a>.<\/em><\/p>\n<p><br \/>\n<br \/><a href=\"https:\/\/martech.org\/how-to-archive-your-universal-analytics-historical-data\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Another Google Analytics 4 migration project deadline is fast approaching, and this deadline is hard set. On July 1, Google will delete all historical data from Universal Analytics properties. This deadline also affects Analytics 360 customers. With little more than one month until the deadline, if you have not done so by now, your organization [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":7256,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5],"tags":[],"class_list":["post-7641","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agency"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.8 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to archive your Universal Analytics historical data - OK Design<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/okdesign.ca\/en\/how-to-archive-your-universal-analytics-historical-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to archive your Universal Analytics historical data\" \/>\n<meta property=\"og:description\" content=\"Another Google Analytics 4 migration project deadline is fast approaching, and this deadline is hard set. 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On July 1, Google will delete all historical data from Universal Analytics properties. This deadline also affects Analytics 360 customers. 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