When Google Analytics and Data Studio aren’t enough and it’s time to switch to Google BigQuery

Business is increasingly moving online, and 2020 has shown that companies in many industries simply can’t survive without an online presence. Naturally, the more customers there are online, the more online user activities there are and the more marketing analysts need to work with data to get useful insights.

This data must be stored somewhere, processed (preferably in real time), and stored indefinitely. After all, historical data is a real treasure for an experienced marketer.

Let’s figure out when it’s time to move away from standard Google Analytics and Google Data Studio solutions and think about choosing a data warehouse instead.


The philosophy that if everything works, don’t touch it cause you’ll break it is familiar to many people. On the one hand, this statement is very reasonable, but on the other hand, businesses’ needs often quickly outgrow the capabilities of customized services.

Nowadays, the rules of the game are changing at tremendous speed. Mobile and smart devices have complicated marketing and repeatedly increased the amount of data used in it. And this trend isn’t going anywhere, as can be seen from the 2020 Global Media Intelligence Report by GlobalWebIndex.

2020 Global Media Intelligence Report by GlobalWebIndex

The number of devices that produced data for marketing reports increases every day. Accordingly, the volume of data processed by marketing specialists is growing. It’s no longer enough to use information only about sales and advertising campaigns. Marketing reports should consider data from many different sources (advertising services, websites, mobile applications, online stores, offline stores, CRMs, and call tracking systems). At the same time, data that comes from different sources is also structured in different ways.

Standard services that almost all marketers use, such as Google Analytics and Google Data Studio, have their limitations. They aren’t flexible and scalable enough to cope with ever-changing demands. Besides, many companies simply lack the resources to process data. As a result, most information risks going unprocessed and unused.

The top priority for a marketing analyst is to provide their company with high-quality and useful insights as quickly and cheaply as possible. Cloud services and data warehouses play an essential role in this, offering significant scaling solutions and flexibility in terms of settings. Let’s find out how to understand when it’s time to change the tools you’re using.

When does it make sense to use Google BigQuery?

Most companies use well-known and popular services from Google. However, not all services are equally useful, and not all services are suitable or necessary for all companies. It all depends on the size of the business and the industry. Logically, a startup with one landing page and a large omnichannel retailer need different analytical tools. To avoid unnecessary money and time expenditures, a company must clearly understand what it needs.

It’s time to change something within your analytical system when you encounter the limitations of the following services:

Google Analytics

Note! In this article, when we talk about Google Analytics we’re talking about Universal Analytics. Google recently launched its new version of Google Analytics known as Google Analytics 4, and it’s the default option for new users. The next generation of Google Analytics has its benefits and limitations, but for now (as of the beginning of 2021), this product is still being refined, improved, and updated.

If you don’t have much data yet or you’ve just launched your online store, then Google Analytics (i. e. Universal Analytics) suits you perfectly. In the beginning, you can easily download data manually if you have only a couple of ad sources. But as the number of advertising channels and campaigns increases, it’s worth thinking about automation. Otherwise, you’ll find yourself bogged down by routine and boring data transfers. To save time and avoid human errors, you should automate your marketing.

OWOX BI allows you to easily and quickly set up the automatic collection of cost data from different advertising services into Google Analytics and Google BigQuery. Besides, OWOX BI checks UTM tags and automatically converts all cost data to your base currency. You can try the service for free!


Back to the data collection limits of Google Analytics: This service is free and processes a tremendous amount of information worldwide, and quite logically, it places restrictions on data collection. These limits apply to all Google Analytics collection tags, libraries, and SDKs.

If you have a small business or a startup with an advertising budget of up to $100,000 a year, you have nothing to worry about. It’s unlikely you’ll exceed the service limits. But those who have businesses with advertising budgets of $100,000 a year or higher should be careful. You can easily exceed the limits and lose important information about user behavior as a result. You need to be extremely careful with these limits:

  • Measurement Protocol, Android SDK, iOS SDK, gtag. js, and analytics. js — 200,000 hits per user per day and 500 hits per session
  • Web Property, Property, Tracking ID — 10 million hits per month per property
  • Mobile snippets, ga. js, and any other legacy tracking library — 500 hits per session

Useful links

Google Analytics Collection Limits and Quotas

Data limits for Universal Analytics properties

Of course, it’s quite difficult to miss a message from Google Analytics about exceeding limits and restricting new data collection. But the goal is to not sit and wait for this to happen and then run around and panic. It’s essential to be aware of these limitations and have an action plan for when you reach them. In fact, when you hit these limits, you can move in three directions:

  1. Move to the paid version of Google Analytics.
  2. Stay with the free version, but reduce the number of monitored parameters.
  3. Design a custom analytics system specifically for your business using data gathering connectors, cloud storage, and visualization services.

Read about our experience implementing Google Analytics 360 and Google Cloud Platform services for large e-commerce projects.

Data Studio

Google Data Studio is an excellent data visualization service with native integrations with other Google products and many advantages:

Data Studio also includes lots of filters, page- and report-level elements, calculated fields, simple sharing options, and many more features.

You can use this service at two levels:

  • Basic. Create reports based on data from Google Analytics.
  • Advanced. Create reports based on data from different data sources (internal CRM system, cost data from advertising services).

Learn how to build advanced marketing reports in Google Data Studio.

Useful links

Welcome to Data Studio (tutorial)

Google Data Studio Tutorial for Beginners

Google Data Studio Tutorial — How to build a Dashboard from MeasureSchool

How to Create a Google Data Studio Dashboard from Social Media Examiner

Data Studio is excellent for small companies and startups if you need to set up a visually understandable and elegant dashboard based on one or two data sources. But don’t forget that this service is designed for data visualization. Among its shortcomings are a lack of support for Excel files (data from Excel has to be connected manually), the low speed of automatic dashboard updates, and a lack of complex visualizations using many data sources.

Data Studio capabilities are enough for most midsize companies, provided they work with a single data source. In other words, you need to upload data from different sources into the same data storage where it will be processed and then uploaded to Data Studio.

But for large corporations with advertising budgets of more than $1 million per year, the amount of data processed is simply too large for this free service. To address this data dilemma, you can use Google BigQuery, which gives companies the power to process petabytes of data in a matter of minutes or even seconds.

Learn how to build an analytics system for your business and why martech tools and analysts are essential.

What is BigQuery?

Different businesses (even if they’re from the same niche) have different requirements for marketing analytics — sales funnels, frequency of purchases, and approaches to brand promotion and customer retention. It’s worth noting that the development of Google BigQuery has made big data analysis available for all companies in the market, not only large corporations.

Google BigQuery is a fully managed serverless data warehouse that enables safe and scalable analysis of petabytes of data. Also, being part of the Google Cloud Platform (a Leader in Data Management for Analytics according to Forrester Research), the service has built-in integrations with Google products.

Google BigQuery

Google BigQuery is simple and fast, and a vast number of specialists can work with it. It also comes with ready-made sets of SQL queries so you can get useful insights from your collected data. Among its other advantages are:

  • 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 helps to remove the burden on companies to manage, control, maintain, and secure data warehouse infrastructure. This allows organizations to focus on achieving business goals.

Why is Google BigQuery the perfect data lake for marketing?

Useful links

What is BigQuery?

BigQuery in a minute

Also, don’t forget that when you create an analytics system for the marketing department, you should always focus on two 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.

When working with Google BigQuery, you can be sure these conditions are met. Although we consider this service a real find for a marketing analyst, it cannot be called a flawless one. Google BigQuery limits the number of incoming requests, the number of updates to a table per day, and so on. To avoid unnecessary routine and tedious work, we recommend setting up automatic data importing from all data sources you need.

Large market-recognized connectors such as OWOX BI have worked with Google BigQuery for many years. OWOX BI collects and merges data (from Google Analytics, advertising services, websites, offline stores, call tracking systems, and CRM systems) into Google BigQuery. As a result, you receive all your data in a uniform structure and can use it to create any reports.


Integrating data with Google BigQuery

If you decide to use Google BigQuery to explore your data, then note that the first step toward doing this is to accurately identify all the data sources you’ll need to work with. This may include various services, platforms, and applications such as Google Analytics, advertising services, websites, offline stores, call tracking systems, and CRM systems. For many companies, this is the primary challenge to using BigQuery.

Note that to automatically upload data from non-Google products, you’ll need a platform for processing and transferring data such as OWOX BI Pipeline, which offers popular and custom connectors for everyone.

Many marketers are frightened by BigQuery because they have to wait for reports to be prepared by analysts or know SQL. OWOX BI is designed specifically for marketers who have data stored in Google BigQuery data storage.

OWOX BI Smart Data combines your data in the right format for your business model and allows you to easily build reports in a simple report designer. Data is valuable only when it gives your business an edge. You can focus entirely on your business goals while Smart Data cares for your data sources and data structures and considers your business model. This product gives a solution for marketers to build reports in a few clicks without SQL queries.

Get ready-made marketing reports without any coding! By using the simple OWOX BI Report Builder interface, you don’t need to understand how your data is structured or wait for a response from your analysts. Just select the dimensions and metrics you want to see in your report and Smart Data will instantly visualize your data in a way you can understand.

Smart Data

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 a service 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 workload to 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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  • What are the benefits of using Google BigQuery in conjunction with Google Analytics and Google Ads?

    By integrating Google Analytics and Google Ads data with BigQuery, organizations can perform advanced analysis on their marketing data. This includes identifying trends, tracking ROI, and optimizing marketing campaigns for better performance.
  • How does Google BigQuery compare to traditional data warehouses?

    Unlike traditional data warehouses, BigQuery is serverless, scalable, and flexible. It also allows for ad-hoc querying and real-time data streaming, making it suitable for data analytics in modern business scenarios.
  • When should I consider switching to Google BigQuery?

    You should consider switching to Google BigQuery if your organization is experiencing slow query performance due to large datasets, data silos, or complex queries. BigQuery also allows for cost-effective scaling, enabling organizations to pay only for the resources they need.