SAS Intelligent Decisioning
Make the best data-driven decisions when interacting with customers, partners, suppliers and employees.
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Deliver real-time decisions for all your use cases
Deliver relevant, interactive, decision intelligence based on business rules and sophisticated analytics at scale to limitless use cases. Easily test decisions before deployment to ensure they deliver the expected results. Robust governance ensures your decisions are understood and trusted across the business. High data and decision volumes are easy to handle with a cloud-based architecture that can scale to meet your demands. Deliver decisions at the edge and in stream through integration with IoT devices.
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Foster collaboration for users of all skill sets
Easily build and modify decisioning processes, whether you’re a data scientist who codes, a data science team working collaboratively to solve big problems or a business user who prefers a drag-and-drop GUI. Define decisions by browsing centralized data sources, models and business rule repositories and selecting from existing assets. Compare different objects and versions to understand and track changes for easier cross-team collaboration. Ensure accuracy and task transparency for decision makers using an out-of-the-box, role-based governance workflow to apply various decisioning checkpoints.
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Make the best decision every time
Improve decision quality and increase your competitive advantage by incorporating artificial intelligence and machine learning models from a common repository, created in your preferred language.
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Deploy analytical models in decisions quickly & consistently
A common decision authoring and deployment environment dramatically reduces the time required for IT to validate and deploy analytical models – whether written in SAS, open source or custom code. You can natively integrate, manage and deploy SAS and Python analytical models, custom code, external data sources and business rules from a single interface with identical logic for batch and real-time web service execution. That means faster deployment and confidence in the integrity of your analytically driven operational decisions.
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Leverage the power of SAS Intelligent Decisioning inside the Microsoft Power Platform
The SAS Decisioning Power Connector, a certified connector in the Microsoft Power Platform, enables you to link to your SAS Viya environment and run selected decision intelligence modules and analytical models within a Microsoft Power App or Power Automate. The SAS Decisioning connector easily combines the two leading platforms for AI-driven business intelligence and processes.
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Quickly execute more decisions with less effort
SAS Container Runtime is a lightweight OCI (Open Container Initiative) compliant Docker container for executing SAS models, Python models and Python code. It is highly scalable and can be published to any container registry on Microsoft Azure, AWS and GCP. Users can then use the cloud infrastructure to deploy with the smallest footprint. This ensures you can fully utilize your resources to quickly execute the largest number of data-driven decisions and reduce the effort required to manage the environment.
See SAS Intelligent Decisioning in action
Organizations of all sizes are working smarter with SAS Intelligent Decisioning
Key features
Easily create, manage and govern robust, analytically driven business rules to power data-driven decisions at scale.
Data access, preparation & quality
Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded artificial intelligence.
Data visualization
Visually explore data and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data points.
User-friendly interface
Build and manage even the most complex decisioning elements with ease – including decision flows, rules, data sources code nodes and analytics – using a drag-and-drop GUI or writing code in the language of your choice.
Common decision authoring & deployment environment
Simplify validation of the entire decision flow. Complete version control preserves documentation, testing traceability and continuity in an environment shared by IT and the business.
Real-time decision intelligence
Apply analytics and business rules in real time during customer interactions and operational business processes.
Reusable content
Create reusable content and custom functions to accelerate decisioning across the organization.
Rapid, well-governed decision intelligence life cycle management
Quickly respond to changing data sources, decisions and customer dynamics. Speed up common manual tasks, such as business rule definition and deployment, in a controlled and safe environment to ensure decision intelligence quality and auditability. Easily integrate your organization's CI/CD processes.
Integrated with SAS machine learning & model management capabilities
Use with visual machine learning for integrated model development and management capabilities – both SAS and open source. A common model repository facilitates centralized model governance and monitoring.
Cloud native
SAS Viya's architecture is compact, cloud-native and fast. Whether you prefer to use a public or private cloud provider, you'll be able to make the most of your cloud investment.
Better together with Microsoft
The SAS Decisioning Power Connector enables you to include decisioning as part of the Microsoft Power Platform. You can easily embed models and decisions in web and mobile apps. Automate business processes with intelligent decisioning. And centrally manage business rules.
SAS Viya est une solution cloud-native et cloud-agnostic.
Consommez SAS comme vous l'entendez - SAS managed ou autogéré. Et où vous voulez.
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- SAS® Model ManagerSimplify the creation and management of your model collection with a web-based interface that easily automates the model management process.
- SAS® Viya®Bénéficiez d'une intégration plus rapide des données, d'un développement efficace des modèles et d'une réduction des coûts liés au cloud.
