GA 4 x BigQuery – The next step in Digital Maturity

Mark Lee, Technical Analytics Director

GA 4 x BigQuery – The next step in Digital Maturity

Mark Lee, Technical Analytics Director

GA 4 x BigQuery – The next step in Digital Maturity

Mark Lee, Technical Analytics Director

GA 4 x BigQuery – The next step in Digital Maturity

Mark Lee, Technical Analytics Director

GA 4 x BigQuery – The next step in Digital Maturity

Mark Lee, Technical Analytics Director


The quest for digital maturity is a complex and evolving process, with the pace of change quickening and an economic backdrop which is increasingly competitive, having the right roadmap is pivotal. It has been found that “only 9% of businesses use insights and technology to create useful, relevant experiences at multiple moments across the purchase journey” (Boston Consulting Group – Digital Maturity Benchmark Digital Maturity Benchmark).

In other words, less than 1 in 10 businesses are not able to reap the same benefits as the companies that do invest and upskill in new technologies, such as; 2X more likely to grow market share, an average 29 ppts in cost savings or 18 ppts increase in revenue*. These are substantial gains to be had by any size business, showing the scale of opportunity for growth in an increasingly demanding space.

Google Analytics 4 can be the first step in many companies’ road to digital maturity, offering marketers and analysts a view on web and app performance, along with some campaign reporting.  In this post I’d like to talk about how the integration with BigQuery should be your next step in digital maturity, to elevate your insights and activate from a holistic or granular approach dependant on your needs.

What is BigQuery?

Google BigQuery, like many Google services, was originally designed to enable Google to both store and analyse data at a petabyte scale. It was then released so that businesses and individuals can utilise the technology as well. To query data, analysts can use one of the most comment languages, Standard SQL, to write queries to uncover insights. This helps to lower the barrier to entry and democratise data for markets at scale. The key benefits can be summarised as;

  • Speed – data can be analysed quickly in seconds or minutes, as opposed to hours or days from non-cloud based solutions. This enables companies to uncover and react to insights sooner
  • Scale – through its place in the cloud it can quickly scale to meet demands placed on it when large datasets are stored or queried
  • Cost – with a pay-as-you-go payment option, users can easily control costs and set limits. Moreover, its costs for usage are competitive helping to save business money
  • Native integrations with Google products – being a Google product means that it can integrate with a few clicks to many of Google’s Marketing Platform products (Analytics, Ads, Search Ads, Campaign Manager etc). Unlocking cross-platform analysis and advertising.
  • Unsampled Data – it never samples data, unlike platforms such as Google Analytics, meaning analysis is conducted on 100% of the available data every time. This provides reliability in the data, and confidence that decisions made from it are based accurately
  • Visualisation integrations – there are a wealth of integrations with visualisation offerings from Tableau, PowerBI, LookerStudio and many more. With this, users can convert the data from BQ into easily digestible charts and tables for consumption

The GA 4 and BigQuery Integration

There are a few methods of sending the data to BQ from GA4;

  • Daily – which will send batched data for the previous day once a day, at around mid-day based on the time-zone set in the GA 4 property. This dataset is extensive, and includes all the data available within GA 4 (except item-scoped dimensions and metrics at the time of writing)
  • Streamed – sends data roughly every 5 minutes, enabling a close to real-time view of web or app activity. This method incurs and a cost of $0.05 per GB (1 GB = ~600k GA events), and does not include acquisition data
  • User Data – new – only limited data available within our test space, so still playing with this to form an opinion
  • Both Daily and Streamed – Our recommended approach in many instances, where cost can be absorbed, is to enable both export options. This enables analysts to conduct almost real-time analysis and monitoring of the site or app, while also providing marketers a daily view of campaign performance

The integration between Google Analytics (GA) and BigQuery (BQ) has long been a key pillar to success for many brands. It enables users to access the most granular levels of data from GA, combine data sources such as GA, Ads, CRM and others, as well as leverage Machine Learning to create predictions or generate insights. This opens the door for marketers and analysts alike into Data Science, where new and exciting pipelines can be built to transform existing reporting and activation strategies from single channel to multi-touch.

Why is it the next step in Digital Maturity?

With GA 4, BQ not only allows access to the most granular data captured, but it also removes many of the limitations that exist within the platform. Furthermore, the reporting can be completely customised to suit individual business needs gives brands flexibility and clarity around how data is used and consumed. Examples of this include;

  • Building funnels with greater than 20 steps
  • Viewing unsampled data
  • Building a custom attribution model tailored to your business’ needs
  • Enhance data from other sources, such as other GMP products or internal data
  • View more than 500 unique events
  • View more than 125 event parameters

By integrating with additional platforms, BQ can form a single-source of truth from which all analysis and reporting can be conducted, removing calculation discrepancies from different platforms.

From this marketers, analysts, data scientists, decision makers and more can begin to quantify the key questions around their marketing, content, site and more perform. Strategies can then be formed to optimise, test and scale further into digital maturity.

What’s different with GA 4 x BQ, compared to UA x BQ?

With Universal Analytics (GA3), users were only able to leverage the integration between Google Analytics and BigQuery if they had a 360 license. This meant a substantial number of brands and smaller businesses were not able to reap the benefits of this integration. With Google Analytics 4 however, this integration is FREE for all, enabling all brands to take advantage of its capabilities.

The changes made to GA 4 over UA continue with changes to the limitations and closer to real-time events. Typically, the UA streaming export would be every 15 minutes, with GA 4 this is reduced to just a few minutes; allowing for insights to be analysed quicker when running on-site tests or campaigns which require immediate consumption.

A key limitation which is removed with the integration with BigQuery are the parameter limits. Within the GA 4 platform, there is a limit of either 25 or 125 total parameters which can be send, depending on if you are a standard or 360 customer. If you send more parameters than those limits, you won’t be able to see those within Google Analytics. Within BQ however, you’ll be able to analyse and view them just as you would any other event, further expanding on the scope of what’s possible to track and analyse.

It is worth noting though, that if you are exceeding these limits, you should question where it is necessary. One of the main benefits of GA 4 is that it forces a focus on KPI’s which are truly valuable, and not track everything just because it’s possible.

What should brands be doing now?

If you’ve not already, you should link your GA 4 property to BQ as early as possible. This is because currently, the integration will not back-fill and will only process data from the point of linking. Therefore, if the linking has not already been made, you could be losing data from which you can later analyse and query within BQ.

If you have created the link, you can begin to take advantage of the unsampled data and granularity it offers. If costs are a concern, the free-tier within BQ allows for 1 TB of query usage per month, while Google also providing some sample queries to help get you started here.

Once comfortable using the platform, we recommend integrating more data sources to form a single-source of truth, from which all analysis can be collated and sent.

How can Merkle support?

We have been among the first to support businesses migrating to GA4, and how they can prepare for the future. If you require further support and guidance on how to migrate, setup or implement GA 4 or BigQuery with a best-in-class team, please reach out to us at