If you are kicking off your first BRMS project, don’t start by gathering the rules! Often teams will be advised to begin their business rules project by gathering all the relevant rules, in a natural language or rulebook approach. But these rules-first approaches address issues that don’t exist with modern BRMS technology, resulting in redundant and counter-productive […]
Watch the demonstration of the integration capability of DecisionsFirst Modeler Enterprise Edition with IBM Operational Decision Manager (ODM). DecisionsFirst Modeler enables organizations to accurately specify their business using decision requirements models; structure and manage the supporting business rules; and streamline business process design. The Enterprise Edition integration with IBM ODM delivers traceability from business objectives through […]
Watch the demonstration the integration of DecisionsFirst Modeler Enterprise Edition with RedHat JBoss BRMS. DecisionsFirst Modeler is a collaborative decision modeling solution using the new Decision Model and Notation (DMN) standard. DecisionsFirst Modeler provides a diagram-based, business user friendly front-end to the business rules environment. The newly available DecisionsFirst Modeler Enterprise Edition Integration with JBoss BRMS: […]
One of the most important steps in a predictive analytic effort is correctly framing the problem a way that creates a shared understanding of the business problem across business, IT, and analytics teams. A decision requirements model makes it clear how to best leverage analytics.
Leading organizations today are looking to scale their advanced analytics capabilities, especially data mining and predictive analytics, to improve business performance, reduce fraud and improve customer responsiveness. However traditional analytic project approaches are hard to scale and difficult to implement in the real-time environment required in modern enterprise architectures. Learn best practices from leading organizations […]
Learn how to achieve a standards-based, efficient approach to analytics deployment from open source like R and commercial analytics products to a wide range of platforms including mainframes like IBM zSystems and new data infrastructure like Hadoop. The big challenge for analytics-driven organizations today is closing the gap between deriving an analytic result and getting […]