Posted By: Meri Gruber | Posted On: 23rd September 2014 |
Brian McDonough (@briantheanalyst) at IDC just published an IDC MarketScape: Worldwide Decision Management Software Platform 2014 Vendor Assessment. Brian also posted to the IDC blog about the study, Making Analytics Actionable: Decision Management Software Platforms, where he makes several interesting observations: The gains in corporate performance from improving high-volume decisions were impressive. The processes targeted with decision […]
Posted By: James Taylor | Posted On: 17th September 2014 |
Brian McDonough has been writing (with Dan Vesset, Steve Hendrick, Henry Morris and others) on Decision Management at IDC for many years and he has recently published an IDC MarketScape: Worldwide Decision Management Software Platform 2014 Vendor Assessment. The report has an IDC MarketScape figure, IDC’s opinion on the Decision Management market, process and software as well as some […]
Posted By: James Taylor | Posted On: 21st August 2014 |
Gregory over at KDNuggets had an interesting post with some Top Analytics and Big Data trends ahead of Strata Hadoop NYC Conference based on input from their readers. Three trends struck me: The challenge of communicating complex analyses to non-technical clients/partners We are having increasing success using logical decision models to show how data and analytics drive better […]
Posted By: James Taylor | Posted On: 1st August 2014 |
There was a fascinating piece in the Economist last week – Little things that mean a lot. This piece really resonated with me – even the title sounds a lot like my mantra of “Big Data, Little Decisions” (you can see a selection of the articles and webinars I have given on this topic here). So what were the critical points Schumpeter made?
First the point that constant experimentation and rapid iteration is critical when trying to get value from all this data. Experimentation is often the skill we tell clients they most need to develop and we regularly stress the importance of putting in place the infrastructure and processes for ongoing decision results analysis. To maximize the value of big data and beco
Posted By: James Taylor | Posted On: 21st May 2014 |
Live Training as part of Brainstorm San Francisco
The Decision Model and Notation (DMN) is a standard for decision modeling in the process of being adopted by the Object Management Group. It provides a graphical notation for specifying decisions so that decision making can be modeled separately from business processes. DMN allows both technical users and business users to describe, model and manage decision-making by providing a notation that is intuitive to business users yet able to represent complex decision making. The DMN specification also provides a mapping between the graphics of the notation to the underlying constructs of execution environments such as business rules management systems and predictive analytic environments.
The primary goal of DMN is to provide a standard notation that is readily understandable by all business stakeholders. These business stakeholders include the business analysts wh
Posted By: Meri Gruber | Posted On: 20th May 2014 |
Manifeste de la Gestion de Décisions – the French Translation of The Decision Management Manifesto white paper is now available. Thank you to Emmanuel Bonnet, Directeur du Conseil, Génigraph, for providing the translation. The goal of the Decision Management Manifesto to help organizations like yours design, build and implement decision management systems with business rules and predictive analytics. […]
Posted By: Meri Gruber | Posted On: 15th May 2014 |
Manifiesto de Gestión de Decisiones – the Spanish Translation of The Decision Management Manifesto white paper is now available. Thank you to our Alliance Program Partner Plugtree for providing the translation. The goal of the Decision Management Manifesto to help organizations like yours design, build and implement decision management systems with business rules and predictive analytics. The manifesto […]
When it comes to analytics, many organizations focus on using analytic insight to improve executive decision making. Yet there is often an even greater opportunity when using analytics to improve operational decision making. Operational decisions about a single customer or transaction are made by call centers, local staff, and automated systems. These decisions affect everything from fraud to customer satisfaction, from risk management to resource utilization. Although each decision has a localized impact, enterprises make so many of these decisions that the cumulative impact is enormous.
Big data makes it hard to scale human interpretation, forces faster and more precise decision making, and puts a premium on advanced analytics. Applying big data analytics requires a clear and direct focus on managing decision making, especially in operations. But most organizations lack an approach that lets them specify their requirements, and their ability to find opportunities for, and successfully use, advanced analytics is limited. This course lays out a framework to identify the op
The right time to make decisions for organizations is increasingly real time. Customers want responses in real time; supply chains must adapt to disruption in real time; fraud must be caught before it gets into the system while self-service and Web applications can’t wait for human intervention. At the same time, organizations have discovered the value of analytic, data-driven decisions. The challenge is to reconcile these demands—to provide real-time, analytic decision making.