BigQuery reporting standard

Maximizing the Potential of GA4 Data Beyond UI Limitations

Published: Jan 22nd 2024 | 5 min read

Why can't Google Analytics 4 user interface reporting cover all requirements?

The main issue with reporting in Google Analytics is sampling and thresholding. This means that you are limited in the depth to which you can analyze your data. There is only one way to bypass sampling and thresholding of data in GA4 - pull data into BigQuery. That way you will always have unbiased data. Since the data in Big Query is a table that contains raw data, it is necessary to prepare basic datasets for reporting and the following use of this data.


Huge customization potential and more sophisticated data utilization through:

  1. Unsampled data
  2. Real-time data (no 2-day delay)
  3. Historical data (no 14-month retention period limitation)
  4. Offline data integration possibilities (user data from CRM, marketing spends etc.)

To use data effectively, we've designed a database structure. This ensures the quality and integrity of the data we use.

Structure description

This structure presents a layered approach to managing GA4 data. Each layer fulfills the specific need of the data management and transformations. That requires specialized expertise, facilitating efficient and accurate data processing and analysis.

High-level view of the database structure and roles required to build it

L0 Raw layer

The L0 Raw Layer contains raw GA4 data. This layer is crucial for retaining the original data structure. It makes it available for further transformations and analysis in subsequent layers. Be aware that raw data from GA 4 are not the data which you see in user interface of GA 4. There is no precalculated metrics such as sessions/users and that needs to be established in next layers.

L1 Normalized Model Layer

In the L1 Normalized Model Layer, GA4 event data undergoes a structured transformation process. It is organized into distinct tables, including events, sessions, users, and more. Normalizing data makes later analyses easier and improves the efficiency and clarity of the data ecosystem.

L2 Business Layer

Within the L2 Business Layer, a deeper dive into data occurs. Advanced analytics, funnels, segmentation, KPI tracking, and data enrichment are the focal points. This layer focuses on extracting useful insights from data to offer valuable information for decision-making and strategic planning.

L3 Presentation Layer

The L3 Presentation Layer is the interface where data is transformed into consumable knowledge. Customized reports and dashboards created here help various departments answer their questions. Simultaneously, it maintains data governance, ensuring data quality and compliance with regulations. This guarantees the reliability and trustworthiness of the presented insights.

Each layer represents a specific part of the database which must be operated by a specialist. You can find out more about who these specialists are below.

Roles description

Database Engineer & Database Architect

  • Proficiency in GA4 Data: Demonstrates a comprehensive grasp of GA4 data intricacies, including metrics, dimensions, and the interconnections among GA4 entities like sessions, events, and users.
  • Mastery of Big Query: Possesses a deep understanding of the Big Query database structure and its extensive features, including database objects and the nuances of the Big Query SQL dialect.
  • Database Architecture Expertise: Proficient in designing optimized database structures and their components, akin to a Database Architect (DBA).

Data Analyst

  • Big Query Competency: Has a fundamental understanding of the Big Query database structure and excels in employing the Big Query SQL dialect.
  • Business Acumen: Proficient in translating and implementing business requirements into actionable data processes.
  • GA4 Data Proficiency: Displays an in-depth understanding of GA4 data, encompassing metrics, dimensions, and the interconnectedness of GA4 entities.

This position doesn’t require as many senior technical skills as the previous one. With that being said, it can be taught in couple trainings with Cross Masters team and be fully operated by the person inside your company.

Business Analyst

  • Visualization Proficiency: Proficient in creating insightful reports using various Business Intelligence (BI) tools such as Power BI or Looker Studio.
  • Data Connection Expertise: Possesses knowledge of connecting report datasets, with a clear understanding of the process involved in linking BI tools to the Big Query database.

To be able to fully comprehend all the business requirements this position should be covered by someone who understands the business processes inside the company (team). The best choice in this case is your team member.


In summary, this approach ensures the data is being used in correct and efficient way but requires a certain level of expertise. At Cross Masters, we have the expertise and capacity to fulfill all of these roles or provide comprehensive training for them. Our team's proficiency in GA4 data, Big Query, database architecture, and business analytics ensures that we can design, build, maintain, and optimize these databases effectively. Furthermore, we can tailor training programs to empower your internal team members, particularly for roles like the Business Analyst, which benefit from an in-depth understanding of your company's unique business processes.

Our goal is to partner with your team either by direct role fulfillment or skill development opportunities. We adapt to your specific needs, ensuring your data management system is not only technically correct but also aligned with your business objectives.

Eliminate all GA4 reporting issues and make the most of your data.
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