{"id":60001,"date":"2022-08-31T07:00:00","date_gmt":"2022-08-31T14:00:00","guid":{"rendered":"https:\/\/eigene-homepage-erstellen.net\/?p=60001"},"modified":"2022-09-01T14:33:49","modified_gmt":"2022-09-01T21:33:49","slug":"how-to-use-bigquery-to-fill-in-the-gaps-in-google-analytics-4","status":"publish","type":"post","link":"https:\/\/eigene-homepage-erstellen.net\/blog\/analytics\/how-to-use-bigquery-to-fill-in-the-gaps-in-google-analytics-4.htm","title":{"rendered":"How to Use BigQuery to Fill in the Gaps in Google Analytics 4"},"content":{"rendered":"\n

By now, you’ve most likely heard the news that Universal Analytics will stop processing hits on July 1, 2023.  Google recommends that all Google Analytics properties move over to Google Analytics 4 as soon as possible. 

One of the biggest, most impactful benefits of this move is the exporting of Google Analytics data into Google BigQuery.  BigQuery allows scalable data warehousing and data architecture needs that integrate with the rest of your organization’s data.  Using the rich data sets of Google Analytics in Google BigQuery can help you mine more impactful insights about your organization and your customers. <\/p>\r\n\r\n\r\n\r\n

What Is BigQuery?<\/h2>\r\n\r\n\r\n\r\n

BigQuery is a product of Google Cloud and serves as a cloud data warehouse that allows you to query large datasets quickly and efficiently.  Google Analytics 4 is an event-based measurement protocol.  GA4 can count every action taken on a website, from page load to mouseover to form fill, as an event.  This permits you to export all of your raw events from Google Analytics 4 properties to BigQuery.  You can then use SQL queries to query that data or combine that data table with other data tables in your organization.<\/p>\r\n\r\n\r\n\r\n

Why You Should Connect GA4 With BigQuery<\/h2>\r\n\r\n\r\n\r\n

One of the immediate benefits of Google Analytics 4<\/a> is the BigQuery export.  Exporting all events into BigQuery is a must-have when setting up GA4.  The standard reports GA4 can provide you with some instantaneous data, such as page visits, event loads, and conversions.  That’s a good start, and your organization likely has other data sources in other locations, so I recommend setting up your connection to GA4 to BigQuery as soon as possible.  Always keep in the back of your mind how this data can connect to other data sets in your organization.

The Export Integration step-by-step can be found in the Analytics Help of Google<\/a>. 

Once I did that, I turned my attention toward implementing some of the reports that were readily available in Universal Analytics.  This article explores how and why we want to build three queries based on reporting needs.  After we query, we can then make them into a table and pipe the data into Google Data Studio.<\/p>\r\n\r\n\r\n\r\n

Queries to Improve Your GA4 Report<\/h2>\r\n\r\n\r\n\r\n

With the direct connector to GDS, we want to explore 3 queries based on some of the reports that were more readily available from Universal Analytics.

Each Google Analytics 4 property has a single dataset named “analytics_<property_id>” is added to your BigQuery project.  Within each dataset, a table named events_YYYYMMDD<\/span> is created each day if the Daily export option is enabled.  I highly recommend enabling the daily export option.  Once exported, you can find a list of all available columns in the GA4 BigQuery Export schema<\/a>. <\/p>\r\n\r\n\r\n\r\n

1. How to Create a BigQuery Table<\/h3>\r\n\r\n\r\n\r\n

Any query in BigQuery can be turned into a table.  That table can then serve as your data source in a Google Data Studio report.  Visit Google’s documentation<\/a> for more details on how to create a table in BigQuery. 

After the query is run, click on the “Save Results” drop-down in the Query Results section.  You will have 7 options available.  Choose the one named BigQuery table to then serve as a standalone table. <\/p>\r\n\r\n\r\n

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