SAS Intelligent Decisioning Features | SAS Finland

SAS® Intelligent Decisioning Features

Enterprise scale decisioning

  • Delivers more than 5,000 real-time transactions per second.
  • Response times of 10 milliseconds per transaction.
  • SAS data access engines simplify integration with a variety of third-party applications at the data level.

Decision builder

  • 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.
  • A graphical drag-and-drop interface lets you assemble business rules, custom code and models into complex decision flows, which minimizes the need to write deployment code that joins these pieces together.
  • 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 condition logic (i.e., IF-THEN-ELSE) and using outputs from any preceding rule or model.
  • 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.
  • 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.

AI/ML augmented decisions

  • Simplify model inclusion by seamlessly drilling 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 and R.*
  • Apply governance workflows to models through SAS Model Manager.*
  • Directly include Python models 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.

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.

Collaboration

  • 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.

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.

Enhanced term management

  • Automated rule-to-term mapping includes type and domains from existing data dictionaries and tables.
  • Rename terms and choose what to include/exclude from the input and output the definition of a decision flow.
  • Dynamically add new terms as needed to simplify term definition, data type, input/output designation and length.

Streamlined deployment to multiple destinations

  • Real-time deployment (via REST API):
    • 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.

Visualization

  • 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.

Chatbot creation

  • Create and deploy custom conversational chatbots through an intuitive, low-code visual interface.
  • Get text responses, access data, reports and visualizations and even apply analytics and AI through a natural language, conversational interface.
  • Configure bots within the SAS environment for easier access to insights or connect to external services to roll them out to the world.

*When SAS Model Manager, SAS Visual Machine Learning or SAS Visual Data Science is licensed with SAS Intelligent Decisioning.

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