SAS Decision Manager is SAS’ platform for decision automation and is getting a significant update in December 2017. I wrote a product review of SAS Decision Manager in 2014 and a number of things have changed in the new release, which is on the new SAS Platform and leverages new SAS Viya technologies.
SAS Decision Manager is aimed at an analytics ecosystem that is a moving target these days with cloud-enabled analytics that are more open and API-driven, more people doing data science, and different kinds of data coming to the fore. Meanwhile IoT is adding new data streams and demanding decision-making at the edge while machine learning and AI are hot trends and offer real possibilities.
“If analytics does not lead to more informed decisions and more effective actions, then why do it at all”
Mike Gualtierei, Forrester.
This quote embodies the need to operationalize these analytics and enable faster decision making. SAS believes, as we do, that one must put analytics into action, operationalize your analytics, to get value. You need to go from data to discovery and to deployment. In this context, SAS Decision Management is a Portfolio to create and manage decisions:
Overall architectural view
- SAS Model Manager – import and govern models, monitor and retrain models, deploy models. And increasingly any kind of models including R, Python…
- SAS Decision Manager – build business rules, build decisions that use analytics and rules in a decision flow, deploy as decision services. The SAS Business Rules Manager product has been subsumed into the new SAS Decision Manager product to create a single environment.
- SAS Event Stream Processing Studio – SAS Event Stream Processing Studio is now in the SAS Decision Management portfolio so that decisions can be injected into the streaming data environment – real time as micro services but also into streams.
- Execution – covers Cloud Analytic Service (Viya) for testing and deployment as well as model training, Micro Analytic Service for REST ESP for streaming data, and in-database or in-Hadoop.
- Plus, open APIs to allow REST, Python, Lua, Java and CLIs to access the platform. R and PMML can be brought into the modeling tools too.
SAS Decision Manager wraps business rules, analytic models, flow logic (and soon Python) into services while linking to Model Manager to access the models being used. These models are developed in the new SAS VDMML Model Studio. The new release of SAS Decision Manager is built on the new SAS Platform, which brings the benefits of the new platform around cloud readiness, multi-tenant etc. This release also combines the Business Rules Management offering in SAS Decision Management.
Key elements overall include:
- Visual Decision Modeling – decision simulation and path tracing, model and business rule integration and streamlined business rules management
- Unified publishing to ESP, Cloud Analytic or Micro Analytic services, in-database or in-hadoop
- Model Manager integration to make it easier to share models and support for more kinds of models as well as managing publishing of models to multiple end points (e.g. in IoT) and automating updates etc.
- Open APIs from Viya, workflow etc.
Some specific improvements for SAS Model Manager
- Common Model Repository with GUI and REST interfaces to manage content and search to find the right models
- Can register models from SAS VDMML Model Studio and import models from PMML, Python, Zip files, etc.
- Model publishing to various defined targets from in-DB, In-Hadoop, SAS, streaming or real-time with SAS micro analytic service.
- Model compare in terms of statistics and plots as well as the definition of champion and challenger.
- Version control with revert, tracking, creation of new versions
SAS Decision Manager
- Decision inventory in a common repository with access to the models in the model repository as well as to the rules available. All these elements are versioned.
- New graphical decision flow editor that brings analytic models from model manager, rules and specific branching logic.
- The testing environment shows how data flows through the decision flow to show which paths were most heavily used. Data can be brought in dynamically or from existing data sets.
- New editor allows direct access to the model or rules from the flow and get access to repository information as the diagram is edited. Rules are managed directly in the same repository
- Can create temporary information items on the fly for use in rules
- Can bring in lookup tables from the SAS data environment
- Ruleset editor allows data to be pulled in as the vocabulary (copying from another or accessing the data layer) and then rules can be written.
Test data results showing which elements of the decision flow have the most transactions.
In addition to the December released, the plan is to move to more regularly update the product with a 6 month cycle for with new algorithms, more integrations, more use of the Viya APIs etc.
You can get more information on SAS Decision Manager here.
