A key tenet of Enterprise Decision Management is the automation of the operational decisions that drive your business. You need to identify operational decisions and automate them;…
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by jonathanlewisforcongress.com
A key tenet of Enterprise Decision Management is the automation of the operational decisions that drive your business. You need to identify operational decisions and automate them; you also need to separate them out from the rest of your applications so that they can be managed and reused. The best way to do this is to design what I call Decision Services – services in your Service Oriented Architecture that automate and manage highly targeted decisions that are part of your organization’s day-to-day operations.
The first thing a Decision Service does is isolate the logic behind these business decisions, separating it from business processes and the mechanical operations of procedural application code. Treating decision logic as a manageable enterprise resource in this way means you can reuse it across multiple applications in many different operational environments. A centralized approach to decision automation can also eliminate the time, cost and technical risk of trying to reprogram multiple individual systems simultaneously to keep up with changing business requirements. For instance, the rules for paying an insurance claim can be removed from the definition of the claims processing business process. These rules can be managed independently, important as the legislative change cycle is different from the business cycle that drives process change. They can also be re-used, for instance to help customers tell if they have a valid claim before submitting it or to support third-party agents. The same rules can be in multiple decision services.
Having a decision in a single Decision Service makes it easier for you to improve that decision over time. Not only is your ROI higher – improvements in the decision improve results in all the applications that use the Decision Service, change is quicker and easier to manage.
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This allows you to apply predictive analytics, for instance, to the decision-making process you are automating so as to make the decision more precise. Indeed, with experience, you can apply more advanced analytics and consider the mathematical relationships between business objectives, actions, customer reactions, constraints and outcomes. This lets you take market and economic uncertainties into account and arrive at optimal decision strategies quickly while still isolating any needed changes to a single service or component.
A Decision Service can be used to provide a “brain” to your composite applications. Composite applications are an effective way to assemble existing, working functionality to serve a new business purpose. You can put together bought, built or legacy components and create new applications more quickly. Decision Services, plugged into this approach, allow you to make these composite applications “smarter” and less reliant on people for decision-making. Sometimes this will make it easier to connect existing services, sometimes it will let you take more advantage of the services you have. There is also a cultural component to the managing Decision Services in this way. By recognizing and separating out operational decisions, you can focus your business thinking and investment on these decisions more easily. You can apply your strategic vision and proper management approaches to drive toward optimal decisions.
There is an interesting article on Decision Services by Larry Goldberg on the BPM Institute site – A SOA-based Business Rules Approach: Decision Services and I have a couple of related posts and articles:
Others talking about decision services include Fair Isaac, Oracle (whose BPEL process manager allows Decision Services to be integrated as noted here) and ILOG.
Here are some examples from various industries to try and make it clearer:
An auto policy underwriting service that combines pricing rules and risk models to underwrite policiesA cross-sell offer service that combines segmentation rules and response models to generate the best cross sell offer across channelsA fraud detection service that uses rules and a neural net to identify fraudulent credit card transactionsA shipment routing service that routes shipments to customers based on demand, contracts and statusA claims payment service that uses rules to check a claim for authorization to pay while also applying analytics to detect potential fraudAn eligibility service that allows citizens to check their eligibility for benefits and applies state and federal rules appropriatelyA return authorization service that tells front-line retail staff whether a particular customer can make a particular return using return rules, purchase history and future revenue potential
