List of Google BigQuery ML Customers
Google
1600 Amphitheatre Pkwy,
Mountain View, 94043, CA,
United States
Mountain View, 94043, CA,
United States
1 650-253-0000
Professional Services
198933
$402.8B
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 Distribution organisation with 57700 employees and revenues of $222.58 billion, The Home Depot, a United States based Retail organisation with 470000 employees and revenues of $152.67 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 | Distribution | 57700 | $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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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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Tenet Healthcare | Healthcare | 74480 | $20.7B | United States | Google BigQuery ML | ML and Data Science Platforms | 2022 | n/a |
Tenet Healthcare implemented Google BigQuery ML in 2022 as a core component of its ML and Data Science Platforms strategy to centralize predictive analytics in support of compliance and operational risk functions. The implementation was led from a Compliance Analytics Manager role focused on reducing external consulting and third party engagements through inhouse model development and automation.
The deployment combined Google BigQuery ML model development with broad ETL orchestration across Google Cloud Platform and Microsoft Azure, with explicit management of service accounts and data connection controls. Functional capabilities implemented included supervised and unsupervised model training in Google BigQuery ML to detect clusters of potential fraud, kickback, conflict of interest, discrimination, and other priority incidents, automated reporting and audit pipelines, and dozens of operational dashboards to surface KPIs for business leaders.
Integrations were built to consume disparate incident reporting sources and workforce data such as employee engagement and policy training records, and to provision analytic outputs to visualization tools including Tableau and Power BI and to IT service tooling such as ServiceNow. SQL performance work was completed across BigQuery, Netezza, and Azure Synapse to optimize reporting and dashboard responsiveness, enabling near real time access patterns for business partners and stakeholders.
Governance emphasized ETL process integrity, service account lifecycle management, and role based data access controls to support crossfunctional use by compliance, audit, risk, and HR teams. The program automated several dozen reporting and audit processes, eliminated manual data manipulation for stakeholders, and delivered new corporate cost savings through reduced consulting and third party engagements while generating hundreds of work productivity hours saved each week.
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Retail | 470000 | $152.7B | United States | Google BigQuery ML | ML and Data Science Platforms | 2017 | 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, The 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 Distribution, Retail and Healthcare, 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.