Introduction to Google BigQuery

👋 BigQuery is a data and analytics warehouse on Google Cloud. Here you'll discover the benefits of exporting data to BigQuery, as well as use cases.

What is BigQuery?

‍BigQuery is a Google Cloud analytics data warehouse designed to help companies turn their big data into business enablement. BigQuery enables you to store large datasets and launch ultra-fast SQL queries thanks to Google's computing power.

Why export data to BigQuery?

• ✅ Become the owner of your data to process and refine it for more relevant analysis.

• ✅ Avoid losing user data that has been inactive for more than 2 to 14 months, when GA4 will delete it.

• ✅ Avoid cardinality problems encountered in the GA4 interface or by the data API (e.g. native Looker Studio connector)

• ✅ Don't be limited by GA4's interface or data API, with which you'll always encounter threshold problems.

• ✅ Store and join other data sources, such as advertising data, offline data and CRM data to perform queries joining multiple data sets.

• ✅ Have access to raw data from GA4 or advertising platforms to perform deeper analyses on conversion attribution and deduplication.

• ✅ Use real-time data to steer marketing strategies at key times of the year.

• ✅ Preprocess data for predictive analysis to better forecast consumer demand.

• ✅ Work retroactively on GA4 data sets, which is not the case with filters and conversions in GA4. You can also filter or modify incorrect tracking to clean up and prepare the data.

Become the owner of your data

In fact, the data presented in the interface is only a small portion of the analysis potential offered by the data collected in Google Analytics. BigQuery allows you to take ownership of your data, process it and refine it for more relevant analysis.

GA44 limits raw data storage to 14 months.

This means that if you want to keep your data beyond this period, you'll need to export it to BigQuery or another data storage system.

☝ Exports to BigQuery are not retroactive. Only data collected after export will be available in BigQuery. It is therefore important to plan your data export according to your needs and analysis objectives.

Use casesfor exporting data to BigQuery

‍Use external data for advanced analysis

As BigQuery is a data warehouse, it is possible to store other data sources such as advertising data, offline data and CRM data in order to carry out queries linking several datasets. Linking different types of data will enable you, for example, to compare your sales with the margin achieved over a given period. These analyses can then be sent to a data visualization tool (Google Data Studio) to obtain illustrated reports combining your Google Analytics data and external data.

A global view of conversion attribution

Thanks to BigQuery's data export functionality, you can access raw data from advertising platforms and perform deeper analyses on attribution and conversion deduplication. These analyses can provide a consolidated view of attribution in a data visualization tool, enabling you to make rapid budgetary decisions across different platforms.

Using real-time data for marketing strategies

It takes just a few seconds to transfer the Google Analytics 4 data stream to BigQuery. What's more, data is transferred continuously (approximately every 15 minutes). This makes it easier to steer marketing strategies at key times of the year when every minute counts (such as Black Friday for example).

Predictive audience creation

In BigQuery, you can use SQL queries to pre-process data for analysis to better predict consumer demand.

BigQuery features integrated Machine Learning(BigQuery ML) functions to perform this analysis. This will enable you to increase your customer retention rate by capturing signals of attrition(Churn Modeling) or conversion(Conversion Modeling), LTV etc., more effectively and more quickly.

You can activate these audiences via your various partners (CRM, Media, Paid, Social, etc.).