List of SAS Visual Data Mining and Machine Learning Customers
SAS Institute
100 SAS Campus Drive,
Cary, 27513-2414, NC,
United States
Cary, 27513-2414, NC,
United States
1 800-727-0025
Professional Services
13500
$3.6B
Since 2010, our global team of researchers has been studying SAS Visual Data Mining and Machine Learning 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 SAS Visual Data Mining and Machine Learning 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 SAS Visual Data Mining and Machine Learning for ML and Data Science Platforms include: Rogers Communications, a Canada based Communications organisation with 24000 employees and revenues of $20.60 billion, Shionogi, a Japan based Life Sciences organisation with 5200 employees and revenues of $3.00 billion, ATB Financial, a Canada based Banking and Financial Services organisation with 5044 employees and revenues of $2.20 billion, Wake County, a United States based Government organisation with 4372 employees and revenues of $1.56 billion, SciSports, a Netherlands based Leisure and Hospitality organisation with 41 employees and revenues of $4.0 million and many others.
Contact us if you need a completed and verified list of companies using SAS Visual Data Mining and Machine Learning, 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 SAS Visual Data Mining and Machine Learning 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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ATB Financial | Banking and Financial Services | 5044 | $2.2B | Canada | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, ATB Financial implemented SAS Visual Data Mining and Machine Learning. The deployment used SAS Viya on Google Cloud as a unified and agile end to end ML and Data Science Platforms solution to accelerate big data analysis and to improve the customer experience.
Central to the implementation were machine learning models that enable personalization at scale and advanced model management capabilities. SAS Visual Data Mining and Machine Learning provided access to both open source and proprietary algorithms, model lifecycle tooling for training and versioning, and in memory and in database processing to support faster experimentation and scoring.
The technical architecture placed SAS Viya compute on Google Cloud with Hadoop employed as a data store, combining scalable cloud compute with distributed storage for large analytic workloads. Operational ownership emphasized analytics and customer experience teams as ATB shifted from developing tools to providing services, enabling practitioners to apply cutting edge techniques and to scale up quickly to meet performance needs.
Governance centered on model management and service oriented delivery rather than ad hoc tool use, embedding repeatable workflows for training, validation, and deployment. The in memory and in database processing reduced batch processing time by half, and the Google Cloud deployment with Hadoop as a data store produced a 25 percent performance improvement.
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Rogers Communications | Communications | 24000 | $20.6B | Canada | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, Rogers Communications deployed SAS Visual Data Mining and Machine Learning as part of a program to modernize its SAS Analytics suite. The implementation is categorized under ML and Data Science Platforms and was positioned to centralize predictive analytics capabilities for customer experience use cases.
The deployment emphasized core functional modules typical of the category, including data preparation and feature engineering, interactive visual model building, automated model tuning and algorithm selection, and model management and scoring services. The SAS Visual Data Mining and Machine Learning rollout included capabilities for model operationalization to support reuse, iterative experimentation and production scoring.
Rogers leveraged a 25 year relationship with SAS to align the new platform with its existing SAS Analytics estate, keeping architecture and tooling consistent with prior investments. Operational scope extended across multiple internal teams responsible for customer experience, analytics, marketing, product management and operations, providing role based access to insights for a workforce of roughly 24000 employees.
Governance and workflow changes were implemented to enable cross team collaboration while protecting analytic assets, including role based access controls, model cataloging and review workflows for data scientists and business stakeholders. The initiative focused on delivering accessible insights to business teams to improve the customer experience, with centralized analytics stewardship and phased adoption to manage model lifecycle activities.
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SciSports | Leisure and Hospitality | 41 | $4M | Netherlands | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, SciSports deployed SAS Visual Data Mining and Machine Learning to operationalize BallJames, its 3D player tracking and rendering pipeline. SciSports used SAS Visual Data Mining and Machine Learning as an ML and Data Science Platforms application to drive deep learning based image recognition and 3D production workflows for sports analytics.
The implementation centered on model lifecycle capabilities, including in memory training of convolutional neural networks and production inferencing for object classification that distinguishes players, referees and the ball. Functional modules implemented included deep learning model training, model scoring and management, and image recognition pipelines tailored to sequential camera feeds.
SAS Event Stream Processing was used to enable real time image recognition, and models were trained in memory on SAS Viya with the flexibility to train in the cloud, on cameras or where compute resources existed. The architecture supported pushing trained models onto cameras for edge inferencing while retaining a uniform platform for cloud based training and centralized model management, preserving both streaming ingestion and low latency scoring.
Operational governance consolidated model orchestration and the 3D production chain under a single platform to standardize deployment and monitoring. Program stakeholders characterized the ability to deploy deep learning models in memory onto cameras and perform inferencing in real time as cutting edge science, and stated that without SAS Viya, this project would not be possible.
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Shionogi | Life Sciences | 5200 | $3.0B | Japan | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2025 | n/a |
In 2025, Shionogi implemented SAS Visual Data Mining and Machine Learning as part of a platform initiative in ML and Data Science Platforms across its Data Science Department. The deployment was built on SAS Viya and produced an AI-driven layer called AI-SAS that semiautomatically generates SAS programs to support regulated clinical trial analysis and reporting.
The implementation centralized machine learning and deep learning capabilities within SAS Visual Data Mining and Machine Learning, including use cases that relied on convolutional neural networks and other deep learning techniques. Engineers used Python workflows exposed through the SAS Viya interface to produce readable, SAS-native program output, enabling the majority SAS-skilled programming staff to consume advanced models without creating a black box. The configuration focused on program generation automation, model training and inferencing pipelines, and reusable analytical templates for repeated trial tasks.
Operational coverage targeted clinical trial analytics and the broader drug development lifecycle, with the Data Science Department driving semiautomation of routine SAS programming tasks that previously consumed substantial hours per trial. The system was applied to regulatory-compliant analysis and reporting workflows and is being adopted by other Japanese pharmaceutical companies and contract research organizations, with intentions to expand globally. Shionogi also plans to extend the approach into human resources, health management and real world data analysis.
Governance and workflow changes formalized a pattern of delegating repetitive program production to AI-SAS while retaining human oversight for safety, efficacy and compliance reviews. The program templates and model artifacts were governed within the SAS Viya environment to ensure reproducibility and auditability, aligning evidence generation around SAS as the central analysis tool. This alignment supports Shionogi’s strategic shift toward a health care as a service, HaaS business model.
Results reported by Shionogi include a reduction in standard analysis time per trial of 30 percent, equating to approximately 100 hours less analysis time per trial from a prior baseline of 350 hours. The company cites improved efficiency in clinical trial analytics and recognition as a Noteworthy DX Company by the Ministry of Economy, Trade and Industry, outcomes that informed further internal adoption and external interest from peers in the pharmaceutical ecosystem.
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Wake County | Government | 4372 | $1.6B | United States | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, Wake County implemented SAS Visual Data Mining and Machine Learning to construct a repeatable tax assessment model. The deployment used SAS Visual Data Mining and Machine Learning as an ML and Data Science Platforms solution to support the analytical life cycle from data to discovery to deployment.
The implementation configured end-to-end data mining and machine learning workflows, leveraging the application’s comprehensive visual interface for data preparation, feature engineering, model training, model assessment, and production scoring. SAS Visual Data Mining and Machine Learning was used to create standardized modeling templates and visual model comparison processes, enabling analysts to iterate and validate alternative algorithms within the platform.
Operational scope focused on Wake County tax assessment and property valuation business functions, with the model designed for adoption by other governments with minimal customization. The analytical workstreams were centered on assessor office workflows and county valuation operations, with the platform providing repeatable model artifacts that can be parameterized across jurisdictions.
Governance emphasized lifecycle management and repeatability, embedding standardized model development patterns, deployment pipelines, and documentation practices to support cross-jurisdiction reuse. The implementation prioritized production-ready deployment capabilities and model governance within the ML and Data Science Platforms environment to reduce customization effort for other government adopters.
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Buyer Intent: Companies Evaluating SAS Visual Data Mining and Machine Learning
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating SAS Visual Data Mining and Machine Learning. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating SAS Visual Data Mining and Machine Learning for ML and Data Science Platforms include:
- Symphony Risk Solutions, a United States based Insurance organization with 112 Employees
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| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD SAS Visual Data Mining and Machine Learning Coverage
SAS Visual Data Mining and Machine Learning is a ML and Data Science Platforms solution from SAS Institute.
Companies worldwide use SAS Visual Data Mining and Machine Learning, from small firms to large enterprises across 21+ industries.
Organizations such as Rogers Communications, Shionogi, ATB Financial, Wake County and SciSports are recorded users of
SAS Visual Data Mining and Machine Learning for ML and Data Science Platforms.
Companies using SAS Visual Data Mining and Machine Learning are most concentrated in Communications, Life Sciences and Banking and Financial Services, with adoption spanning over 21 industries.
Companies using SAS Visual Data Mining and Machine Learning are most concentrated in Canada, Japan and United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of SAS Visual Data Mining and Machine Learning across Americas, EMEA, and APAC.
Companies using SAS Visual Data Mining and Machine Learning range from small businesses with 0-100 employees - 20%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 60%, and global enterprises with 10,000+ employees - 20%.
Customers of SAS Visual Data Mining and Machine Learning 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 SAS Visual Data Mining and Machine Learning 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.