The Benefits of Integrating BigQuery with Google Analytics Data

(Source: Dataedo)

Importance of Analytics Data for Business

Analytics data is crucial for businesses that want to make informed decisions and develop more effective marketing strategies. It provides valuable insights and information about everything from customer behavior and interests to website performance. Google Analytics is a tool that gives users a wealth of data on website traffic, user behavior, and demographics. However, its reporting interface has some drawbacks, particularly with data sampling and quota limits.

This is where BigQuery steps in. BigQuery is a cloud-based data warehouse solution that offers businesses advanced data analysis capabilities. With BigQuery, businesses can access raw, unsampled data, create custom reports, and analyze large amounts of data – including data from Google Analytics – in real-time.

BigQuery Gives Business Access to Raw, Unsampled Data

One of the benefits of integrating BigQuery with Google Analytics is the access to raw, unsampled data. Raw data is untouched and unprocessed, with almost infinite potential. The purpose of gathering raw data is to more accurately and thoroughly discover valuable insights for your business. In contrast, sampled data is only a subset of the dataset, meaning that it may not entirely represent your business’s performance accurately.

In Google Analytics 4, all standard, out-of-the-box reports are unsampled. However, sampling may occur with advanced reporting features and if the data exceeds the maximum event volume of 10 million. Integrating BigQuery with Google Analytics means that sampling is no longer a concern. With BigQuery, businesses can access their raw data and transform it at a detailed level to gain a more accurate and holistic understanding of user behavior and website performance. This can provide more reliable insights and lead to more effective decision-making. For example, businesses can analyze the success of their marketing campaigns, identify trends and patterns in user behavior, and learn how to reach their target audience better.

Create Unique, Customizable Reporting with BigQuery

As mentioned, access to raw data means the sky is the limit for reporting options in BigQuery. BigQuery allows businesses to create custom reports that suit their needs, some of which may exceed the limited customization options within the GA4 reporting interface. Standard reports are not a one-size-fits-all solution because every business is unique. Custom reports allow companies to more efficiently analyze the dimensions and metrics that are actually relevant to their business objectives and goals, therefore gaining more actionable insights and the ability to make smarter decisions.

Advanced reports are available in GA4 though they are subject to data sampling. In addition, Google announced schema compatibility changes that no longer allow some dimensions and metrics to work with one another in GA4. For example, you could not create a report that shows source and medium, both attribution dimensions, and transactions, which is an event-scoped metric:

In BigQuery, you can bypass these compatibility issues.

Another reason to use BigQuery for reporting in Looker Studio is the quota limit issues that surface when using GA4 as the data connector. These measures prevent users from overloading the Google Analytics API with requests. You can read more about these quota limits in our blog, How to Resolve GA4 Quota Limit Configuration Errors.

Perform Advanced Analysis with BigQuery

BigQuery offers more advanced analysis capabilities than Google Analytics. For instance, businesses can create predictive models to forecast future trends with machine learning. They can also perform customer segmentation to understand comprehensive user behavior and identify customers based on their needs and preferences. These more complex analyses can help businesses improve their overall performance by providing deeper insight and identifying new opportunities for growth.

Google Analytics Migration Services

At Marcel Digital, we offer Analytics Migration Services to help businesses integrate Google Analytics with BigQuery. Our team of experts can navigate the complexities of data integration and help your business leverage the full potential of BigQuery to discover deeper insights. Contact us today to learn how our services can empower your business to make data-driven decisions.

Frequently Asked Questions About BigQuery

What is BigQuery?

BigQuery is a cloud-based data warehouse solution that enables businesses to store and analyze massive amounts of data in real-time.

What are the benefits of using BigQuery?

Some benefits of using BigQuery include access to raw, unsampled data, customizable reporting, and advanced analysis capabilities.

How does BigQuery work?

BigQuery works by storing data in a distributed, columnar format, which is a structure that allows for fast querying and analysis.

What types of data can be stored in BigQuery?

BigQuery can store structured and semi-structured data, including CSV and JSON files. Most data stored in BigQuery is table data.

How secure is BigQuery?

BigQuery is a highly secure platform that uses multiple layers of security to protect your data. It includes network encryption, data encryption, access controls, and regular security audits and compliance certifications.

How does BigQuery integrate with other Google products?

BigQuery integrates seamlessly with other Google products, including Google Analytics, Google Ads, and Google Cloud Storage. It is easy for businesses to combine data from multiple sources and gain a more comprehensive view of their performance.

What is the pricing model for BigQuery?

BigQuery uses a pay-as-you-go pricing model, meaning businesses only pay for the data they use. Pricing is based on the amount of data processed, with discounts available for longer-term commitments and higher usage volumes.

How can businesses get started with BigQuery?

Businesses can start with BigQuery by signing up for a Google Cloud account and creating a BigQuery project. From there, businesses can upload data and run queries to gain insights and make data-driven decisions.