Five reasons why you should use BigQuery for your Google Analytics data.

May 6, 2020

BigQuery export for Google Analytics is one of the most powerful tools you can use to understand and get the most out of your data for proper marketing and business decisions. But what exactly makes BigQuery so impressive? And why should you consider investing your precious time and effort in the export configuration? Below, I’ll list my top five reasons why you should start using BigQuery with your analytics data forthwith and which benefits you will get.

1. Reporting flexibility

The first advantage you’ll enjoy when using BigQuery is the incredible reporting flexibility. With BigQuery, you can create any report you dare to imagine. For example, you can include as many dimensions as you want in a report, and these dimensions can be of a different score. Any segments can be applied to the reports, and these segments can be more flexible as well (e.g., forget about 90 days limitation for user-based segments, and use them for cases like calculating audience intersection). In BigQuery, it’s possible to extend the functionality of many standard Google Analytics reports. For instance, you can easily get rid of the assisted conversions report restrictions and include more dimensions in your reports.

2. No sampling

Sampling is a major Google Analytics limitation, but this only applies when you are working in the GA web interface. In BigQuery, you have access to raw hit-level data, so all the reports you build won’t have any sampling by default.

3. Reporting speed

Besides not sampling the data, BigQuery is also extremely fast. Even the vast majority of unsampled reports (if you are a 360 user) take considerably longer to calculate than they do with BigQuery, which is almost instant.

Thanks to Google’s fast cloud-based infrastructure, BigQuery can go through billions of rows and return an accurate result in seconds. If you save some of your regularly used queries and run them automatically (with the help of Google App Script, for example), you can save the result to the auto-updated table and visualize it with Data Studio. For data visualization tools, it’s always easier to build charts based on tables. This approach can, therefore, often be a faster and more convenient alternative to usual GA reports.

Query result

4. Integration with other sources of data

Enriching and combining your Google Analytics data with other data sources is undoubtedly a great idea. Keeping your data in BigQuery will allow you to merge it with your CRM data, product usage statistics or your marketing and advertising costs. This, in turn, gives you the opportunity to build a robust end-to-end analytics system, which will become a cornerstone for data-driven decisions.

5. A better understanding of data

If you start using BigQuery, you will certainly gain new knowledge and a better understanding of how Google Analytics data is structured and stored. There is a noticeable difference between session-based web reports and hit-level data in the warehouse. Being aware of this difference empowers you as a digital analyst, and eventually, you will be able to take full advantage of your data. Working with BigQuery also requires you to write SQL queries and sort out some BQ-specific concepts (like nested and repeated fields), which can be hard to grasp at first. However, take your time, and when you figure it out, you’ll be a much stronger data user, capable of getting much more out of web and digital analytics systems in general.