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| Company Size | Count |
|---|---|
| Small Business | 8 |
| Large Enterprise | 15 |
IBM Cloud Pak® for Data is a fully-integrated data and AI platform that modernizes how businesses collect, organize and analyze data to infuse AI throughout their organizations. Cloud-native by design, the platform unifies market-leading services spanning the entire analytics lifecycle. From data management, DataOps, governance, business analytics and automated AI, IBM Cloud Pak for Data helps eliminate the need for costly, and often competing, point solutions while providing the information architecture you need to implement AI successfully.
Building on the streamlined hybrid-cloud foundation of Red Hat® OpenShift®, IBM Cloud Pak for Data takes advantage of the underlying resource and infrastructure optimization and management. The solution fully supports multicloud environments such as Amazon Web Services (AWS), Azure, Google Cloud, IBM Cloud™ and private cloud deployments. Find out how IBM Cloud Pak for Data can lower your total cost of ownership and accelerate innovation.
SAS Viya Platform is an advanced data management and analytics tool designed to deliver powerful insights and foster collaboration across teams. It provides flexible and scalable solutions for data analysis, perfect for tech-savvy professionals seeking robust analytics capabilities.
SAS Viya Platform enhances data-driven decisions with its cloud-enabled analytics capabilities. By supporting open-source integration and visual data manipulation, it caters to a diverse range of analytical needs. Its innovative approach to machine learning and AI streamlines operations, helping companies achieve efficient data utilization strategies across departments. Offering robust technical support, it facilitates seamless implementation and rapid adoption, making it ideal for informed analytics exploration.
What are the key features of SAS Viya Platform?SAS Viya Platform is widely adopted across industries like banking, healthcare, and retail, offering tailored analytics solutions to address industry-specific challenges. In healthcare, for instance, it streamlines patient data insights, while in banking, it optimizes fraud detection processes. In retail, it enhances customer experience analytics, demonstrating its versatile application.
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