How to centralise Google Analytics 4 data to gain valuable insights
Get more out of your web data with Analytics Engineering
- Article
- Analytics Engineering
- Data Analytics
Is your web data not fully usable for your Data Analysts? Discover how Analytics Engineers, with their background in web analysis and technical expertise, bridge the gap between technology and business. Centralise your Google Analytics 4 data and enable your entire organisation to quickly and efficiently extract valuable insights.
GA4 data = unstructured data
GA4 data lacks a clear structure. The data is stored in one large table containing all your web data. Extracting the correct data from this massive table requires a lot of technical knowledge. In practice, many Data Analysts struggle with the technical complexity and are unable to extract all the necessary insights from GA4. Data Engineers may need to build a data model, but they are not always available. Additionally, they prefer to focus on tasks such as setting up data infrastructure or building data pipelines.
How to make GA4 data usable for Advanced Analyses with Analytics Engineering
Do you want to enable your Data Analysts to quickly and easily use the correct, structured GA4 data for advanced analyses? And do you want to combine online data with offline data? Then you need to send your GA4 data to BigQuery first. Subsequently, an Analytics Engineer can transform the raw data into various tables containing specific information, such as your marketing campaigns or conversions. By adding this data to your data warehouse, you make it suitable for combining with other (offline) data sources.
The required tools and knowledge for Analytics Engineering
Centralise Google Analytics 4 data with dbt
At Digital Power, we primarily utilise the open-source tool dbt (data build tool). This isn't a data warehouse, but rather a platform where you can write and store your code. Dbt runs on your data warehouse and assists with everything related to the data transformation process.
One of dbt's strengths is its ease of documentation. There's ample space for clear descriptions per column, you can collaborate with multiple people simultaneously, and you have full control over version control. Additionally, we observe that alongside dbt, there are increasingly more tools emerging in the market that could be interesting for Analytics Engineering in the future.
One advantage of dbt is that it becomes easier to improve data quality through automated testing. Additionally, you quickly gain insight into dependencies in the data and models, setting up documentation is simple, and it integrates with Git for version control. The latter is beneficial because you often work with multiple people simultaneously on a data model.
Dbt is open source and therefore free. The data remodelling process with the tool primarily consumes time, but this investment is quickly recouped. With dbt's assistance, the data lineage (the 'history' of your data) becomes clear swiftly, allowing you to test whether the data behaves as expected. Dependencies of data models are also built-in immediately. If model C depends on models A and B, it will only run automatically once models A and B are completed.
Analytics Engineers must master Git and advanced SQL. Additionally, they need to have knowledge of your data warehouse, such as BigQuery or Snowflake. Modelling skills are also essential, allowing them to structure tables effectively for the entire organisation to utilise. While Analytics Engineers don't set up the technical landscape of your organisation themselves, they must understand how the data is structured.
The necessary steps to centralise Google Analytics 4 data
In practice, an Analytics Engineering project may look like this:
- Set up the connection between Google Analytics and BigQuery.
- Integrate dbt with BigQuery and Git.
- Write the code in dbt with version control.
- Return the output of dbt to your data warehouse via BigQuery.
- Based on the data in the data warehouse, Analysts create dashboards and reports.
Need help to centralise Google Analytics 4 data with Analytics Engineering?
You can rely on us for expertise in areas such as Data Engineering, Analytics Engineering, and Data Analytics. This enables us to effectively bridge the gap between the technical and business aspects of your organisation.
All our Analytics Engineers have a background in web analysis. They possess extensive knowledge of web data but are also accustomed, as consultants, to engaging with the business. Get in touch to discuss the possibilities.
This is an article by Jelmer van Leeuwen
Jelmer has worked at Digital Power since 2020 as a Data Analyst and developed himself towards an Analytics Engineer. In the past years, he focused more on the technical side of analytics, data transformations, and data modelling.
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