SAS Intelligent Decisioning Features List

Enterprise scale decisioning

  • Delivers more than 7,000 real-time transactions per second.
  • Response times of 5 to 10 milliseconds per transaction.
  • SAS data access engines simplify integration with a variety of third-party applications at the data level.
  • Powerful, containerized publishing to the cloud for simplified deployments to elastic, fast and flexible runtime environments.
  • Publish containers to container registry or to Git depending on your organization’s needs.
  • Models (SAS models, Python models and Python code) and decisions can be executed in an OCI-compliant Docker container using SAS Container Runtime. They can then be published to any container registry for deployment in the cloud.
  • SAS Intelligent Decisioning landing page for quick access to recently used objects, as well as access to the SAS Community, articles and how-to learning modules to speed your mastery of decision management.

Microsoft Power Platform integration

  • Configure a SAS Decisioning connector instance with your SAS Viya deployment URL and credentials.
  • Browse available runtime modules for SAS decisions and modules.
  • Select and execute runtime modules inside Microsoft Power Apps and Power Automate.

Decision builder

  • A low-code, graphical drag-and-drop user interface lets you assemble decisions using business rules, custom code and analytics, minimizing the need to write deployment code that joins these pieces together.
  • Ability to define and reuse decision variables from a variety of sources, including CSV and data tables, as well as supporting complex structures with data grids.
  • Ability to define decisions by browsing centralized data, model and business rule repositories and selecting from existing assets.
  • Create, manage and test custom code within a decision flow to integrate with business application REST APIs, databases, web service calls and open source Python.
  • Control decision orchestration by adding conditional branching logic (i.e., Yes/No, Equal, Range, Like) and using outputs from any preceding step in the decision.
  • Enhanced rule list view provides compressed, easy-to-read rules that let you readily identify logic definitions.
  • Built-in version control for entire decision flows simplifies testing and validation.
  • Configure and view decision flows in linked view as well as referenced view for ease of understanding.
  • Create complex decision flows for both batch and real-time environments, simplifying IT integration and acceptance testing, as well as operational deployment.
  • Use SAS Studio to build custom data queries and pass data to decision nodes.

Business-user-centric rules management

  • Integrated business rule management platform enables fast rule construction, testing, governance and integration within decision flows.
  • Rule version management capabilities improve tracking and governance during deployment, including deep linking to business rules from decision flows.
  • Create complex business logic quickly within decision flows, including on-the-fly term development.
  • Generate where-used reports for variables and objects used within a decision and anywhere within the SAS Intelligent Decisioning environment.
  • Provides freeform rule-logic creation with full access to sophisticated functions, both predefined and user-defined.
  • Incorporate data quality functions in business rules.
  • Lookup table integration to execute lookup for rule-logic checks and rule actions.
  • Lookup table management for table import and updates gives you the ability to create lookups from SAS Visual Analytics tables.
  • Lookup tables can be activated and locked at user discretion to support proper usage of the most current lookup tables within business rules.
  • Lock down or augment rule versions.
  • Create and manage a common rule repository for simplified business rule reuse.

AI/ML augmented decisions

  • Simplify model integration and inspection with deep linking from decision flow through to model repository in SAS Model Manager.
  • Supports all SAS models (including computer vision and text analytics) and models developed in open source frameworks such as Python.
  • Apply governance workflows to models through SAS Model Manager.
  • Directly include Python models or Python code in decisions via code node.
  • Execute models natively, without the need for translation.

Decision testing

  • Ease of creating test cases by bringing in data from a variety of sources with built-in data mapping tool.
  • Perform scenario tests by interactively entering test values, including an expected result.
  • An autogenerated validation test ensures that testing is run in a manner that resembles the chosen deployment destination.
  • Use a common environment for disciplined testing, change management, auditing and validation.
  • Reporting and user logs for audit history simplifies IT testing for applications that call operational analytics as web services.
  • Register multiple input tables for use within SAS Intelligent Decisioning, including testing, publish target validation and simulation.
  • Save rule tests, test suites and log details for documentation and reuse.

Governance workflows

  • Design, apply and execute various decisioning governance checkpoints from a single location to ensure accuracy and transparency in decision implementations and outcomes.
  • Provides a comprehensive process for executing the various approval pathways required to implement decisions. A graphical interface provides a visual representation of the decisioning life cycle and all parties involved in its development and execution, enabling internal audiences to easily understand a decisioning process's creation, approval and deployment.
  • Free-form comments and labels let you annotate every aspect of the decisioning process. Using labels to build and deploy decisions enables the decisioning community to clearly explain, document and justify each stage of the decisioning life cycle in a single environment, ensuring transparency and fostering trust in decisioning outcomes.
  • Auditing capabilities provide a complete history of decisioning workflows, enabling them to be referenced quickly for internal and external auditing requirements.
  • Enables you to publish decisions to git, which expands your decisioning capabilities beyond SAS offerings. The git repository enables you to access, manage and use decisions across your organization., as well as automate deployment processes via easy integration with DevOps tools.

Decision analysis

  • Use explicit and detailed rule-fire analysis for testing, refinement and rule auditing documentation prior to operational deployment.
  • Graphical tool to analyze and validate decision paths for both development and production.
  • SAS macros to build BI reports with trace information persisted in production for audit.
  • Built-in facility to configure and persist decision variables in database for ease of access for the purpose of decision improvement.


  • Centralized management of all decision assets, including requirements, with role-based access.
  • Reuse decision elements across teams.
  • Compare different objects and versions to understand and track changes for merging branches and easier collaboration across teams.
  • Custom functions and formulas can be shared across the organization via the expression builder.
  • Generate PDF documentation for decisions, rule sets, lookup tables and treatment groups.
  • In a multiuser environment, developers and analysts can now check out the entire decision flow and eliminate conflicting updates. Once done, they can check in with a single operation.

Performance monitoring automation

  • Provide performance reports and notifications.
  • Automatically retrain analytical models when they decay.
  • Swap champion with challenger models based on thresholds.
  • Automate the complete end-to-end model management process.
  • Provide workflows and rules that govern model execution.

Strategy improvement

  • Perform champion/challenger model comparisons.
  • Take advantage of decision analysis capabilities, including simulation options.
  • Track and view the lineage of components to conduct an impact analysis of changes.
  • Get more information about your customers through detailed response history.
  • Strategy performance monitoring and reporting with SAS Visual Analytics.

Streamlined deployment to multiple decisioning runtime environments

  • Real-time deployment (via REST API):
    • Support for SAS Container Runtime for containerized, lightweight deployment based on Open Container Initiative-based (OCI-based) container architecture for lighter-weight, elastic runtime support.
    • Micro analytic web service (MAS) provides fast, scalable web service deployment.
    • Easily move complete decision flows into IT web service testing environments and production deployment.
    • Supporting analytical scoring as a service, MAS execution operates in a self-contained and portable standalone architecture (with a minimal footprint).
  • Scalable and performant batch processing in the cloud:
    • SAS Cloud Analytic Services (CAS) provides a cloud-native, highly performant, batch-processing engine for analytics and decisions.
  • In-database batch deployment:
    • Execute business rules and analytical model scoring without moving the data.
    • Includes extended support for the following Hadoop environments: Cloudera, Hortonworks, MapR, Pivotal and BigInsights.
    • Supports in-database rule execution for models, rules and decisions for Hadoop and Teradata.
  • In-stream deployment:
    • In-memory threaded kernel processing simplifies integration with transactional systems, as well as IoT or in-stream computing.

Data access

  • Seamless, transparent read, write and update access to data, regardless of source or platform.
  • Supports multiple loading options for moving refined data from SAS into third-party data stores.
  • Reuse DBMS metadata for analytical purposes.

Data preparation

  • Machine learning and AI suggestions: Scans data and makes intelligent transformation suggestions using machine learning and AI.
  • Self-service interface: Generates code automatically from an intuitive, point-and-click interface so nontechnical users can profile, cleanse, blend and move data without specialized skills or training.
  • Integration into analytics pipeline: Integrates prepared data into the analytics pipeline automatically, creating a seamless data discovery and data preparation user experience.
  • Data lineage: Explore relationships between accessible data sources, data objects and jobs.
  • Metadata access: Access physical metadata information like column names, data types, encoding, column count and row count to gain further insight into the data.


  • Visually explore data, discover new patterns, and create and share smart visualizations and interactive reports through a single, self-service interface.
  • Leverage augmented analytics and advanced capabilities to accelerate insights and find hidden stories in your data.
  • Easily share insights across channels, such as web, mobile and Microsoft Office applications.