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With GA , users can collect data across multiple channels, including websites, mobile apps, and other connected devices. It offers an event centric approach, allowing you to gather detailed information about every single event that occurs in the customer journey. You might be interested in "Googling AI moving towards Search Generative Experience" The integration of BigQuery and GA BigQuery But how can we take a further step and structure functional and high performance data collection and data modeling strategies? This step consists of using different tools in synergy, such as GA and BigQuery . By leveraging the combined use of these two platforms, we can gain numerous benefits and significantly enhance our data driven strategies. We can combine data collection with normalization mechanisms, business intelligence capabilities and much more. The integration of BigQuery and GA opens up new opportunities.

GA typically offers an intuitive user interface for data exploration and reporting, but it also has photo editing servies some limitations in terms of flexibility in advanced analytics. This is where BigQuery comes in. Thanks to the integration it is in fact possible to transfer the data collected by GA directly into BigQuery for more in depth and personalized analyses, as well as having a significant boost to the data in our possession, also thanks to artificial intelligence and predictive analyses. You might be interested in "Google's evolutions in the Local area" Benefits of using BigQuery and GA together Let's examine the list of resulting benefits Scalability BigQuery is designed to scale efficiently and handle large volumes of data.



This means you can analyze both historical and real time data without performance issues. Advanced analytics With BigQuery, you can perform complex analytics on GA data using powerful query tools. Companies can perform ad hoc analysis, create predictive models and identify hidden patterns to gain a deeper understanding of their customers and their preferences. Google Analytics offers the possibility of collecting a vast amount of data, which can be read and interpreted, but not directly manipulated. However, by transferring this data into BigQuery, it becomes possible to manipulate and use it in various combinations.