List of Google BigQuery ML Customers
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Since 2010, our global team of researchers has been studying Google BigQuery ML customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Google BigQuery ML for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Google BigQuery ML for ML and Data Science Platforms include: Cardinal Health, a United States based Healthcare organisation with 53084 employees and revenues of $222.58 billion, Home Depot, a United States based Retail organisation with 470000 employees and revenues of $159.51 billion, Tenet Healthcare, a United States based Healthcare organisation with 74480 employees and revenues of $20.67 billion, Grupo Herdez, a Mexico based Consumer Packaged Goods organisation with 10500 employees and revenues of $1.89 billion, Springboard, a United States based Professional Services organisation with 3000 employees and revenues of $600.0 million and many others.
Contact us if you need a completed and verified list of companies using Google BigQuery ML, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Machine Learning software purchases.
The Google BigQuery ML customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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20th Century Fox | Media | 2300 | $400M | United States | Google BigQuery ML | ML and Data Science Platforms | 2016 | n/a |
In 2016, 20th Century Fox deployed Google BigQuery ML to accelerate analytics-driven marketing decisions, embedding the platform within its ML and Data Science Platforms estate. Google BigQuery ML was used to operationalize audience segmentation models that produce actionable signals in minutes for downstream marketing workflows.
Implementation centered on in-database model training and scoring, leveraging SQL-driven model construction and predictive pipelines to minimize data movement. The deployment emphasized audience segmentation, predictive scoring, and automated model inference as core functional capabilities, with data science teams configuring model templates and repeatable scoring jobs to support campaign planning.
The implementation included a cross-company use case where Google AI technologies were applied to archival content, specifically working with Iron Mountain archival storage to search and analyze documents held on that service. Operational coverage focused on the data science and marketing functions, with analytics outputs feeding segmentation and decisioning processes used by marketing stakeholders.
Adoption was illustrated publicly through a demonstration by Miguel Angel Campo-Rembado, senior vice president for data science and analytics, showing real time segmentation and faster decision cycles. Governance centered on data science ownership of model configuration and scoring schedules, and on integrating BigQuery ML outputs into marketing decision workflows rather than creating separate analytic silos.
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Cardinal Health | Healthcare | 53084 | $222.6B | United States | Google BigQuery ML | ML and Data Science Platforms | 2018 | n/a |
In 2018, Cardinal Health implemented Google BigQuery ML within its analytics and modeling environment. Google BigQuery ML, part of ML and Data Science Platforms, was used to operationalize SQL first model development alongside Python and ML Tables for data shaping and manipulation.
The implementation emphasized in-database model training and evaluation using Google BigQuery ML, paired with ML Tables to assemble tabular features and Python for preprocessing and custom feature engineering. Workflows incorporated feature engineering, model training, evaluation, and export of model artifacts for downstream scoring, with SQL based pipelines creating repeatable experiment runs.
Deployment integrated Google BigQuery storage, ML Tables, and Python notebooks so analysts could perform data shaping and manipulation directly against enterprise datasets. Operational coverage targeted the companys data science and analytics teams responsible for predictive modeling and analytics delivery, with pipelines operating against centralized BigQuery datasets.
Governance focused on SQL based pipeline versioning and reproducible preprocessing steps, embedding model governance and experiment discipline into existing analytics workflows. The rollout oriented teams toward SQL native modeling patterns and notebook driven preprocessing, aligning development practices with the ML and Data Science Platforms implementation.
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Grupo Herdez | Consumer Packaged Goods | 10500 | $1.9B | Mexico | Google BigQuery ML | ML and Data Science Platforms | 2021 | n/a |
In 2021, Grupo Herdez announced a technological alliance with Google and began deploying Google BigQuery ML as part of a strategic Google Cloud Platform program supported by a planned 15 million dollar investment over five years. The stated objective of the initiative is to transform Grupo Herdez business management through predictive and prescriptive models based on artificial intelligence, accelerating the companys digital transformation and decision making frameworks.
The implementation centers on Google BigQuery ML to create and run machine learning models that generate predictive statistics and prescriptive recommendations, while pairing those models with Looker for visualization and exploratory analytics. This use of Google BigQuery ML falls within ML and Data Science Platforms and ties SQL based model development, model training and model scoring into the companys analytics consumption layer.
Operational coverage targets modernization of infrastructure to better anticipate needs in the production chain and to inform category and operations decision making across Grupo Herdez. The rollout explicitly includes staff training programs to adopt a digital culture, aligning analytics outputs with production planning and commercial processes.
Governance emphasis is on identifying and understanding business processes that can be enhanced with technology, embedding Google BigQuery ML within analytics workflows and Looker dashboards, and upskilling teams to operationalize predictive and prescriptive insights for business management.
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Home Depot | Retail | 470000 | $159.5B | United States | Google BigQuery ML | ML and Data Science Platforms | 2017 | n/a |
In 2017 Home Depot implemented Google BigQuery ML as part of a Google Cloud analytics initiative, leveraging ML and Data Science Platforms to centralize machine learning workloads against its enterprise BigQuery data. The deployment was positioned to support supply chain analytics and real time operational telemetry across retail stores and distribution centers, aligning analytics, engineering, and supply chain teams on a single cloud data platform.
Home Depot’s analysts run Google BigQuery ML to build and operationalize predictive models directly against BigQuery tables, while using AutoML to evaluate and select the best model candidates for forecasting tasks. Functional capabilities implemented include demand forecasting, supplier lead time estimation, and estimated delivery time predictions, and engineers have adapted BigQuery to monitor, analyze, and act on application performance data in real time.
Architecturally the implementation centers on Google Cloud native services with BigQuery as the central data warehouse and in database machine learning via Google BigQuery ML, complemented by AutoML for model selection and experimentation. Operational coverage explicitly includes stores and warehouses, with analytics workflows executed by centralized analytics teams and runtime monitoring handled by engineering teams to enable faster detection and response.
Governance and process changes focused on shifting model development and inference into the data warehouse, enabling analysts to run experiments and engineers to instrument production telemetry without separate model extraction steps. Reported outcomes included more robust demand forecasts, improved supplier lead time and delivery time estimates, and improved security postures, together with more seamless real time application performance capabilities than were previously available on premises.
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Springboard | Professional Services | 3000 | $600M | United States | Google BigQuery ML | ML and Data Science Platforms | 2018 | n/a |
In 2018, Springboard implemented Google BigQuery ML to perform predictive analysis on the Stack Overflow real-world dataset, deploying the solution as part of its ML and analytics capability set. Google BigQuery ML was used to run in-database model training and predictive queries directly against the Stack Overflow dataset, keeping data and model artifacts inside the BigQuery environment.
The implementation leveraged Google BigQuery ML functionality for SQL-based model creation, iterative model evaluation, and in-place feature engineering, consistent with ML and Data Science Platforms workflows. Teams used BigQuery ML model types and evaluation routines to prototype classification and regression experiments, and they persisted models in BigQuery for repeatable scoring and batch prediction.
Operationally the work centered on the data science and analytics organization, with the Stack Overflow real-world dataset ingested into Google BigQuery as the primary data source. Data ingestion and transformation workflows were implemented as SQL pipelines inside BigQuery, enabling analysts to run predictive analysis without moving large volumes of data out of the platform.
Governance was structured around BigQuery dataset access controls and SQL-based model lifecycle practices, enabling controlled experimentation and model versioning within the Google Cloud environment. The narrative emphasizes Springboard Google BigQuery ML implementation within the ML and Data Science Platforms category, and highlights architecture choices that kept modeling, feature engineering, and prediction tightly coupled to the BigQuery data warehouse.
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Healthcare | 74480 | $20.7B | United States | Google BigQuery ML | ML and Data Science Platforms | 2022 | n/a |
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Buyer Intent: Companies Evaluating Google BigQuery ML
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Google BigQuery ML. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Google BigQuery ML for ML and Data Science Platforms include:
- Back Market, a France based Professional Services organization with 1000 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated |
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Back Market | Professional Services | 1000 | $250M | France | 2025-07-16 |
FAQ - APPS RUN THE WORLD Google BigQuery ML Coverage
Google BigQuery ML is a ML and Data Science Platforms solution from Google.
Companies worldwide use Google BigQuery ML, from small firms to large enterprises across 21+ industries.
Organizations such as Cardinal Health, Home Depot, Tenet Healthcare, Grupo Herdez and Springboard are recorded users of
Google BigQuery ML for ML and Data Science Platforms.
Companies using Google BigQuery ML are most concentrated in Healthcare, Retail and Consumer Packaged Goods, with adoption spanning over 21 industries.
Companies using Google BigQuery ML are most concentrated in United States and Mexico, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Google BigQuery ML across Americas, EMEA, and APAC.
Companies using Google BigQuery ML range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 33.33%, and global enterprises with 10,000+ employees - 66.67%.
Customers of Google BigQuery ML include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Google BigQuery ML customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.