The Bigger Truth

    Decision analytics is a transformative technology.

    End-users who do not yet have familiarity and IT experience with concepts like decision management, predictive analytics, and discrete choice analysis are advised to take aggressive steps to quickly move up the learning curve. While some areas of decision analytics such as Bayesian inferencing and adaptive systems do qualify as rocket science, many highly useful aspects of decision analytics are far more easily approachable. However, the scope of decision analytics is wide enough that it takes time and experimentation to fully appreciate how best to leverage these analytical techniques. Pursuing decision analytics is clearly a journey that will take time, investment, and experience. Having a Chief Data Officer (CDO) is a good start, and so is having a decision analytics programs office. Because of the time necessary to develop a decision analytics program with depth (which includes data and decision analytics that reflect policy and result in actions), it is important to start sooner than later. It’s also better to force your competitors to come to terms with decision analytics rather than the unfortunate alternative.

    There are vendors that have decisioning products such as business rule management systems. There are also vendors that provide advanced analytic products. However, there is only a rarified set of vendors that provide both decisioning and analytic products. Why is this important? Although most analytic models are developed offline, some of these models will need to be integrated with decisioning tools in order to address process automation and process improvement needs. To ease this integration process, you will want to look for vendors with explicit experience in both the decision and analytic dimensions of decision analytics to ensure the easiest and smoothest implementation of technology that some will find complex. Due to the large number of categories identified in the decision analytics continuum, enterprises should also look for a vendor that has deep product and service expertise across the decision analytics continuum. As we mentioned earlier, some of the leading vendors in the decision analytics domain include FICO, IBI, IBM, Oracle, Pegasystems, SAP, SAS, and TIBCO. Table 1 provides a list of these vendors and products that support decisioning and analytics.

    Table 1. Leading Decision Analytics Vendors and Products
    Vendor Decisioning Products Analytic Products
    FICO FICO Blaze Advisor, FICO Decision Management Platform FICO Model Builder, FICO Xpress Optimization Suite, FICO Analytic Modeler, FICO Model Central, FICO Decision Optimizer
    IBI iWay Service Manager, iWay Event Manager WebFOCUS Family of Products
    IBM IBM Operational Decision Manager IBM Analytics Decision Management, IBM SPSS Product Family, IBM Cognos Product Family, IBM Algorithmics Product Family, IBM InfoSphere Streams
    Oracle Oracle Business Rules, Oracle Real-Time Decisions
    Oracle Business Intelligence, Oracle Endeca Information Discovery, Oracle Exalytics, Oracle Advanced Analytics
    Pegasystems Pega Decision Management, PegaRULES, Process Commander, Pega Visual Business Director Pega Next Best Action Advisor, Pega Adaptive Decision Manager, Pega Predictive Analytics Director, Pega Firefly, Pega MeshLabs eZi product family
    SAP SAP Decision Service Management SAP BusinessObjects Family, SAP Predictive Analysis, SAP Social Media Analysis
    SAS SAS Enterprise Decision Management SAS Enterprise Miner, SAS Model Manager, SAS Visual Analytics, SAS Visual Statistics, SAS Forecast Server, SAS Contextual Analysis, SAS/OR, SAS In-Memory Statistics for Hadoop
    TIBCO TIBCO Business Events Decision Manager, TIBCO ActiveMatrix Decisions TIBCO Spotfire

    Vendors and end-users should both consider the implications of the decision analytics reference model. The application architecture necessary to deliver the decision analytics reference model involves a number of newer platform-based capabilities such as messaging, event servers, business rule management systems, business intelligence, advanced analytics, push technologies, and application management. Consequently, enterprises are advised to think strategically about their decision analytics needs and the technology stack necessary to deliver on these objectives even if the technology is to be phased in over time.