Exploring BigQuery's Role in Marketing Analytics: A Comparison with Google Analytics 4 and Looker Studio

The shift towards digital business has been unmistakably accelerated by the events of 2020, demonstrating that many industries require a robust online presence to thrive. As more consumers engage online, there is a corresponding increase in online user activities. This surge necessitates that marketing analysts delve deeper into data to extract meaningful insights.

This data must be securely stored, processed efficiently (ideally in real time), and preserved for the long term. After all, historical data is an invaluable asset for a seasoned marketer.

Let’s figure out why BigQuery is needed in addition to Google Analytics 4 and Looker Studio for marketing analysts.

Note: Originally published in January 2021, this post has been thoroughly updated in May 2024 for accuracy and comprehensiveness.

The Need for Advanced Analytics Tools in Today’s Marketing Landscape

The common belief that if something isn't broken, you shouldn't fix it resonates with many. While this approach can be sensible, it often falls short as business needs to evolve beyond what customized services can handle.

In today's fast-changing landscape, driven by mobile and smart devices, marketing complexity has increased significantly along with the data volumes it generates. This trend shows no signs of slowing down, as evidenced by the 2023 Global Media Intelligence Report from GlobalWebIndex.

Each day, more devices contribute data for marketing analysis, leading to more complex data structures and larger volumes needing processing. It's no longer sufficient to analyze just sales and advertising campaign data. Marketing reports must incorporate diverse data sources, such as:

• Advertising services
• Websites, mobile apps
• Online and offline stores
• CRMs
• Call tracking systems

It's important to note that each of these sources may have a different data structure.

While standard tools like Google Analytics 4 and Looker Studio are widely used, they lack the flexibility and scalability that analytics platforms need to meet changing demands. Moreover, many organizations lack the necessary resources to process their data fully, potentially overlooking valuable insights.

The ultimate goal for marketing analysts is to deliver high-quality, actionable insights to their companies efficiently and cost-effectively.

Cloud services and marketing data warehouses are crucial in this context, providing scalable solutions and adaptable settings. Our marketing metrics are collected in an online database, so everyone can easily find them on the same page. It can also help reduce the likelihood of confusion and conflict between departments. The majority of marketing software displays data gathered and calculated from a range of sources.

Let's explore how to recognize when to switch to more advanced tools.

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Introduction to Analytical Tools: BigQuery, Google Analytics 4, Looker Studio

Many companies rely on popular Google services, but not all these tools are universally beneficial or necessary. Their utility can vary based on a company's size and industry. For example, a startup with a single landing page and a large omnichannel retailer will have different analytical needs. To prevent wasting time and money, a company must have a clear understanding of its specific requirements.

Let's closely examine some of the tools - Google Analytics, Looker Studio, and BigQuery that are widely used by marketers in today's business environment.

Marketing Insights: Google Analytics 4

With the introduction of Google Analytics 4 (GA4), marketers and businesses are stepping into a new era of data analysis. GA4 is engineered to handle the complexities of the modern digital landscape, offering a more integrated and insightful approach to understanding user behavior across platforms and devices. Unlike its older versions, GA4 is built with the future in mind, providing robust analytics capabilities that are crucial for navigating today’s marketing challenges.

1. Greater Flexibility and Scalability with GA4’s Architecture

GA4’s architecture is designed to be flexible and scalable, allowing marketers to track a wider range of user interactions. This is especially beneficial as the number of data sources and the amount of data continue to increase.

2. Efficient Management of Complexity through Automation

Automation plays a vital role in efficiently managing this complexity. Tools like OWOX BI seamlessly integrate with GA4, automating data collection and consolidation. This not only saves time but also improves data accuracy by reducing manual errors.

3. No Data Collection Limits for Enhanced Analytics

Furthermore, GA4 does not have the same restrictions on data collection as previous versions, making it a powerful tool for businesses of all sizes. Its ability to handle large amounts of data without session or property limits ensures that businesses can expand their analytics efforts as they grow.

4. Leveraging Automation for Advertising Data

To fully utilize GA4’s capabilities, it's recommended to use automation for collecting and analyzing data from various advertising platforms. This approach allows marketers to concentrate on strategic decision-making rather than being overwhelmed by data management tasks.

As we continue navigating the ever-changing digital landscape, adopting GA4 will become increasingly important. Its advanced analytics features provide a comprehensive view of customer behavior, allowing for more informed marketing strategies and decisions.

OWOX BI enables the quick and easy setup of automatic cost data collection from various advertising services into Google Analytics 4 and Google BigQuery. Additionally, OWOX BI verifies UTM tags and automatically converts all cost data to your base currency. You can even try the service at no cost!

Data Visualization: Looker Studio

Looker Studio (the evolution of Google Data Studio), continues to redefine the landscape of data visualization and analytics with its latest developments. These enhancements aim to empower businesses of all sizes with more dynamic, flexible, and comprehensive tools for analyzing and presenting data.

• Expanded Data Connectivity: Looker Studio has broadened its already extensive support for data sources, making it easier than ever to integrate data from various platforms.
• Enhanced Template Gallery: The platform has introduced new, sophisticated dashboard templates catering to marketing analysts. These templates are designed to jumpstart the data visualization process, allowing users to focus on customization and analysis.
• Advanced Customization Features: With updates to its user interface, Looker Studio now offers more advanced customization options. Users can build reports and tailor their dashboards with greater precision.
• Improved Performance and Scalability: Acknowledging the needs of large corporations and the increasing volume of data, Looker Studio has made significant strides in performance.
• Streamlined Collaboration Tools: Looker Studio has refined its sharing and collaboration features, making it easier for teams to work together on reports and build dashboards.
• Comprehensive Learning Resources: To support users in leveraging these new features, Looker Studio has expanded its library of tutorials and guides.

As Looker Studio continues to evolve, it remains committed to addressing the sophisticated needs of data analysts, data engineers, and marketers. The platform is set to introduce even more powerful analytics capabilities, including predictive analytics and machine learning integrations, which will unlock new insights and opportunities for data-driven decision-making.The latest developments in Looker Studio signify a significant leap forward in data visualization and analytics. With its enhanced features and capabilities, Looker Studio is poised to become an indispensable tool for marketers and analysts looking to navigate the complexities of today's digital landscape effectively.As the platform continues to evolve, it will undoubtedly offer even more opportunities for businesses to harness the power of their data for strategic advantage.

Marketing Data Warehouse: BigQuery

Different businesses, even within the same industry, have unique needs for marketing analytics, such as varying sales funnels, purchase frequencies, and strategies for brand promotion and customer retention. Google BigQuery has democratized access to big data analysis, making it accessible not only to large corporations but to all companies in the market.

Google BigQuery is a fully managed, serverless data warehouse that facilitates the safe and scalable analysis of petabytes of data. As part of the Google Cloud Platform – which Forrester Research recognizes as a leader in Data Management for Analytics – it boasts cloud functions and seamless integrations with other Google products.

Google BigQuery is user-friendly and efficient, allowing numerous specialists to utilize it effectively. It includes a set of pre-written SQL queries, enabling users to derive valuable insights from their data quickly.

Some of its other key benefits include:

• Security and reliability: Control access to encrypted projects or datasets and implement identity access management (IAM).
• Scalability: Tailor data storage to the size, performance, and cost requirements of your company.
• Cost optimization: Get pay-as-you-go pricing options and the ability to predict costs.
• Time to value: Start working with Google BigQuery easily and quickly, explore data to find useful insights, and act faster on new business opportunities.

BigQuery takes care of the need for companies to manage, oversee, maintain, and secure their data warehouse infrastructure. This shift enables organizations to concentrate on attaining their business objectives.

Additionally, when developing an analytics system for the marketing department, it's crucial to prioritize two key factors:

1. Your business should have full access to and control over its data.
2. Data should be presented in an interface that’s convenient, familiar, and suitable for decision-makers.

Getting started with BigQuery may seem like quite the learning curve for someone using it. If you come into BigQuery, that's what you get. No sophisticated data search engine. It's a blank space for the SQL query. Yes. Looker can easily connect BigQuery to BigQuery and automatically understand your database schema. You may prefer using this software instead of relying solely on the reporting features of the data sources (Google Analytics data, CRM, ads).

When using Google BigQuery, you can rest assured that these requirements are addressed. While this service is a valuable asset for marketing analysts, it isn't without its limitations. Even though as a popular data warehouse, Google BigQuery imposes restrictions on the number of incoming requests and the number of table updates per day, among other things. To minimize routine and tedious tasks, setting up automatic data import from all necessary external sources is advisable.

Well-established market connectors like OWOX BI have extensive experience with Google BigQuery. OWOX BI gathers and consolidates data from various sources, including your Google Analytics account, advertising services, websites, offline stores, call tracking systems, and CRM systems, into Google BigQuery. This integration of first-party data results in all your data being structured uniformly, facilitating the creation of comprehensive reports.

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Why Marketing Analysts Need BigQuery Along with Looker Studio and GA4

In the rapidly evolving digital marketing landscape, the involvement of Google BigQuery with Looker Studio and Google Analytics data presents a formidable toolkit for marketing analysts seeking to drive data-driven decisions. BigQuery emerges as a crucial component in this trio, enabling analysts to overcome the limitations of traditional data handling and analysis methods.

• Crunch Big Data: BigQuery excels in processing vast datasets quickly and efficiently, making it invaluable for marketing analysts who need to analyze large volumes of data from multiple sources. Its optimized architecture supports the rapid analysis of terabyte-scale datasets, ensuring that marketers can glean insights in real time.
• Combine Data from Multiple Sources: The modern marketing stack involves numerous martech tools, each generating siloed data. BigQuery enables the consolidation of this disparate data into a single repository, creating a unified source of truth. This consolidation is pivotal for in-depth analytics and comprehensive reporting, allowing marketers to draw insights from a holistic dataset.
• Access All Your Historical Data: Unlike many marketing platforms that restrict access to historical data, BigQuery allows marketers to store and analyze all their historical data indefinitely. This vast repository of historical information is critical for trend analysis and long-term strategic planning.
• Automate Data Refreshes: BigQuery automates the refresh of data snapshots through API connections, which is essential for dashboards that require up-to-date data for accurate decision-making. This automation saves time and reduces the risk of human error associated with manual data updates.
• Conduct Ad-Hoc Analysis: BigQuery, as a data warehouse, supports the creation of custom metrics and dimensions through SQL queries, facilitating deep, ad-hoc analysis. This capability allows marketers to explore their data in novel ways, uncovering unique insights that can inform strategy and optimization efforts.

Comparison Between GA4, Looker Studio, and Google BigQuery

In the world of marketing analysis, the ability to collect, analyze, and visualize data effectively is paramount. Google Analytics 4 (GA4), Looker Studio, and Google BigQuery each play a critical role in this process, offering distinct advantages that cater to different aspects of marketing data management and analysis. Here's how these tools align with the needs of marketing analysts.

BigQuery vs GA4

In the dynamic field of marketing analysis, distinguishing between Google BigQuery and Google Analytics 4 (GA4) is vital. BigQuery emerges as a powerful data warehouse, unparalleled for its ability to store and query massive datasets rapidly. This platform is a useful tool for marketing analysts dealing with extensive data analytics, allowing integration with diverse data sources for a holistic analytics approach.

BigQuery's near real-time processing and capability for advanced SQL queries make it indispensable for deep analysis of data and heavy processing tasks. The cost of using BigQuery depends on the volume of data storage and processing, catering to businesses needing scalable data solutions.

Contrastingly, GA4 specializes in web and app analytics, focusing on user behavior insights across platforms with real-time data collection and reporting. Its design facilitates direct data collection from web and app interfaces, making analytics data more accessible and actionable for marketing strategies.

GA4 is invaluable for understanding user interactions, offering both free and premium options to suit various business data scales. This platform is essential for marketing analysts and data scientists aiming to leverage user data for informed decision-making without the complexities of extensive data warehousing.

It must be noted that data stored in BigQuery projects is raw data from GA4. This is not actually true for reports available via Google Analytics 4. Are the reports for GA4 different? Standard reports in reports use aggregated data and provide stripped-back versions of user information.

BigQuery vs. Looker Studio

For marketing analysts navigating data visualization and reporting, the choice between Looker Studio and Google BigQuery highlights a strategic decision in the data analysis workflow. BigQuery stands out for its warehousing capabilities, providing a robust foundation for petabyte-scale analytics. Its SQL-based customization and integration features enable comprehensive data analysis, making it pivotal for data-driven marketing strategies. The platform's usage-based pricing reflects its capacity for extensive data processing.

On the flip side, Looker Studio excels in data visualization, offering marketing analysts a platform to create interactive reports and dashboards. This tool is key for presenting complex data insights in an accessible and visually engaging format, crucial for communicating findings to stakeholders. Unlike BigQuery's backend focus,

Looker Studio's user-friendly interface and free model make it an indispensable tool for analysts at all expertise levels, offering clear insights from various data sources, including BigQuery. This synergy between Looker Studio and BigQuery as a data warehouse provides a comprehensive suite of tools for marketing data analytics, blending data processing with visualization for strategic insights.

For a clear understanding, we have provided a table to look at the features at a glance:

• Primary Use
  • Looker Studio: Data visualization and reporting.
  • Google BigQuery: Data warehousing and large-scale data analysis.
  • Google Analytics 4 (GA4): Web and app analytics with user behavior insights.
• Data Handling
  • Looker Studio: Creates reports and dashboards from various data sources.
  • Google BigQuery: Processes and analyzes petabytes of data.
  • Google Analytics 4 (GA4): Collects and reports data in real time.
• Integration
  • Looker Studio: Integrates with BigQuery, GA4, and other data sources.
  • Google BigQuery: Can be used as a data source for Looker Studio.
  • Google Analytics 4 (GA4): Can be visualized in Looker Studio for advanced reporting.
• Customization
  • Looker Studio: User-friendly drag-and-drop interface for custom reports.
  • Google BigQuery: Highly customizable through SQL queries.
  • Google Analytics 4 (GA4): Offers user-centric, cross-platform analysis.
• Real-Time Processing
  • Looker Studio: Dependent on the data source capabilities.
  • Google BigQuery: Offers near real-time data processing.
  • Google Analytics 4 (GA4): Provides real-time data collection and analysis.
• Scalability
  • Looker Studio: Limited by the data source and processing capacity.
  • Google BigQuery: Highly scalable, suitable for extensive datasets.
  • Google Analytics 4 (GA4): Scalable to meet the needs of most businesses.
• Analytical Depth
  • Looker Studio: Focuses on visualization rather than deep analysis.
  • Google BigQuery: Designed for complex queries and deep analysis.
  • Google Analytics 4 (GA4): Provides analytics focused on user interactions and conversions.

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Utilizing the Power of Three: BigQuery, Looker Studio, and GA4 for Marketing Analysis

The integration of Google BigQuery, Looker Studio, and Google Analytics 4 (GA4) offers a synergistic solution that maximizes data insights and operational efficiency. BigQuery's robust data warehousing capabilities allow for the storage and analysis of vast datasets, providing the foundation for deep analytical queries and insights. This powerful backend processing complements GA4's advanced user behavior analytics, enabling marketers to track and understand user interactions across platforms in real time.

Looker Studio (formerly Google Data Studio) acts as the visualization layer, transforming the raw, processed data from BigQuery data and the nuanced analytics from GA4 into accessible, interactive reports and dashboards. This enables marketing analysts to present complex data findings clearly and engagingly, enabling data-driven decision-making across the organization.

The collaboration between these tools allows for a seamless flow of data from collection (via GA4), through processing and analysis (via BigQuery) to reporting and visualization (via Looker Studio). This integrated approach enhances the accuracy and depth of marketing data insights and significantly reduces the time and resources required to manage data across disparate platforms.

Marketing analysts can leverage this powerful combination combining data to uncover hidden trends in top audience segments, optimize marketing data strategies, and predict future behaviors, ensuring that their organization remains competitive in a rapidly evolving digital landscape.

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Integrate Google BigQuery with GA4 and Looker using OWOX BI

If you're considering Google BigQuery for analysis, the first crucial step is to identify all the data sources you'll need clearly. This might include a variety of business intelligence services, platforms (google marketing platform), and applications such as Google Analytics 4, advertising services, websites, offline stores, call tracking systems, and CRM data and systems. For many companies, determining these sources is a significant hurdle to utilizing BigQuery effectively.

To facilitate data pipelines and the automatic upload of data from non-Google products, you might need a data processing and transfer platform like OWOX BI Pipeline, which provides both popular and custom connectors.

Many marketers are hesitant to use BigQuery because they rely on analysts to prepare reports or need to understand SQL. OWOX BI is tailored for marketers who store data in Google BigQuery and need easier access to it.

OWOX BI organizes your data according to your business model and features a simple report designer, allowing you to generate reports easily. Data only becomes valuable when it provides a competitive advantage. With OWOX BI, you can focus on your business objectives while it manages your data sources and structures in line with your business model. This tool offers marketers a straightforward solution to generate reports with just a few clicks without needing SQL skills.

Experience hassle-free marketing data reporting without any coding! OWOX BI Report Builder’s user-friendly interface eliminates the need to understand data structure or wait on analysts. Simply choose the dimensions and metrics you wish to include in your report, and OWOX BI will immediately render your data in an understandable format.

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Key Takeaways

• To handle the complexity of modern data-driven marketing, you need to create a marketing analytics environment that suits your business.
• Start with small steps, but have a plan for future development.
• Cloud storage is the best option for a growing business with the prospect of using big data.
• Using cloud data warehouses like Google BigQuery allows you to reduce operating and material costs, ensure the scalability of projects, and take advantage of advanced capabilities including machine learning.
• By migrating your data into BigQuery, you’ll reduce infrastructure maintenance costs and have time for creativity and finding powerful insights and ideas to achieve your business goals.

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