List of SAS Visual Data Mining and Machine Learning Customers
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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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Life Sciences | 5200 | $3.0B | Japan | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2025 | n/a |
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Government | 4372 | $1.6B | United States | SAS Institute | SAS Visual Data Mining and Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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Buyer Intent: Companies Evaluating SAS Visual Data Mining and Machine Learning
- Symphony Risk Solutions, a United States based Insurance organization with 112 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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