My Step-By-Step Blueprint For Learning Google Analytics BigQuery

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Last Updated: February 2, 2022

Following are the main advantages of using Google BigQuery for Google Analytics:

#1 BigQuery is specially designed for storing and querying big data (very large data sets, usually in terabytes). 

#2 Since BigQuery is a data warehouse, it is an excellent tool for combining data from several data sources (like Google Analytics, Google Ads, Facebook etc) for the purpose of advanced reporting and analysis.

Thus the use of BigQuery helps in fixing data integration issues and enables robust cross-device and cross-platform data analysis and reporting.

#3 You can access raw hit level Google Analytics data in BigQuery which is not possible when you use the Google Analytics user interface. 

#4 You can access 100% unsampled data even if you are not a GA360 customer.

#5 You can manipulate Google Analytics data in a way which is many times simply not possible by using the Google Analytics user interface. 

For example, certain dimensions and metrics combinations can not be used/queried together whether you use the Google Analytics user interface or Google Analytics API. BigQuery has no such limitations. 

For example, you can easily combine session-based dimensions (like source/medium) with user-level or page-level dimensions and metrics in BigQuery.

This is one of the biggest advantages of using BigQuery. It makes advanced data segmentation and analysis possible. It removes most of the limitations which come when you use the GA user interface or API for querying analytics data. 

There are companies/analysts out there who use BigQuery a lot more than the GA user interfaces for querying data. 

My Step-By-Step Blueprint For Learning Google Analytics BigQuery

Get the only ebook on Google Analytics BigQuery ever published (60+ Pages)

Yes I want the FREE ebook

#6 There are many limitations when you query user-level data via the GA user interface.

For example, you can not query Goal Conversion rate data by users. But there are no such limitations with BigQuery. You can easily query user-level data in BigQuery. 

#7 When you use the GA user interface, the goals and view filters do not work retroactively. The goal and filter data is collected and reported only from the date you first set up your tracking.

So if you want to calculate say goal conversion rate based on historical data then it is not possible. But BigQuery has no such limitations. You can calculate both goal completions and goal conversion rate based on historical data. 

#8 BigQuery allows you to easily filter out or modify incorrect data from your analysis and reports. But when you use GA user interface to query data, you can not easily filter out incorrect data and you can not modify incorrect data. Data once skewed is skewed for good in the GA user interface.

#9 You can easily integrate BigQuery with data visualization tools like Google Data Studio and can thus easily visualize Google Analytics data.

#10 When you use BigQuery you can easily store and re-run queries thus saving a lot of time in data retrieval. 

#11 BigQuery allows you to query even terabytes or petabytes of data within seconds. Since BigQuery can execute SQL statements very fast, it can be used for real-time analytics. 

#12 You can store a massive amount of data in BigQuery for relatively low prices.

#13 BigQuery is actually easy to learn and use thought it may look quite scary if you have never used it before.

#14 You don’t need to install or set up anything in order to use BigQuery. You can be up and running in a few minutes/hours. 

#15 BigQuery is easy to use with multiple users and teams. 

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