List of Google BigQuery ML Customers
Mountain View, 94043, CA,
United States
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!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
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.
|
|
 |
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.
|
|
 |
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.
|
|
 |
|
Retail | 470000 | $159.5B | United States | Google BigQuery ML | ML and Data Science Platforms | 2017 | n/a |
|
|
|
|
|
Professional Services | 3000 | $600M | United States | Google BigQuery ML | ML and Data Science Platforms | 2018 | n/a |
|
|
|
|
|
Healthcare | 74480 | $20.7B | United States | Google BigQuery ML | ML and Data Science Platforms | 2022 | n/a |
|
Buyer Intent: Companies Evaluating Google BigQuery ML
- 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 | ||
|---|---|---|---|---|---|---|---|---|
| No data found | ||||||||