Decisioning systems need to fit in with existing processing systems.

There are five easy steps for deploying decisions using a combination of three core patterns – patterns suited for Legacy Systems, for Commercial Software Packages and for Modern Decision-Centric Systems respectively. This recipe allows for incremental changes that can be directed systematically for a powerful digital transformation.

Step 1- Pull Decisions out of Processes

Explicitly defined and managed decisions (a) simplify processes by abstracting the embedded business logic, and (b) allow knowledge to be injected into processes in the form of advanced analytical models and business rules.

Organizational decisions are not as effective because we have not been able to explicitly connect organizational Knowledge to decisions. Formal decision modeling helps deploy existing knowledge and highlights knowledge gaps that need to be filled.

Knowledge is diffused across the organization

How do we know if we are making good decisions? When we are confident that we have used all available knowledge in making that decision.

How can we tell if our knowledge is good or bad? There is no clear measure of knowledge but its value is understood and appreciated. We know that there is a lot of effort and investment in increasing organizational knowledge. Procedure Manuals, Training Programs, Collaboration Sites, Intranets, Portals, Share point and others are all geared towards making organizations better through pooling and managing the communal knowledge.

Decision makers are supposed to become knowledgeable on their own

Even though Decision Management technologies are built upon agile development principles, implementation projects frequently get bogged down in traditional approaches. Let us embark on a journey in the hopes of Finding Agile in those murky waters.

Decision Management Technologies are Center Stage

Most of the new advances in technology are aimed at making the systems more intelligent by having them do more knowledge work. These collection of technologies are the Decision Management technologies – that includes business rules, analytics and optimization. Since decision-making is amongst the most fluid aspects of an organization and the logical place to plug knowledge in, all related technologies are experiencing a surge in demand.

Business Decisions and Business Processes need to be analyzed and improved simultaneously for better business outcomes. Following are some practical notes from the field on the correct perspective for this.

1. Decisions are a network or a hierarchy, not a flow

Decision Analysis does not need a flow-based sequential thinking like Process Analysis. Unlike processes, decisions are evaluated in a network where a number of lower-level decisions are made to aggregate up to the main business decision of interest. This state-less decision can be plugged into a process with the process then flowing on down the time dimension.

Decision modeling adds explicit effectiveness metrics to the more generally accepted efficiency metrics generated by business process modeling,giving the full picture to the Executive Suite. Process efficiency measures throughput, costs, error-rates and similar, while process effectiveness measures whether the process is fulfilling its purpose – picking the most profitable customers, for example. Most business process management metrics focus on efficiency measures since effectiveness measures generally depend on the decisioning logic embedded within processes. Explicit decision modeling turns a spotlight on process goals and associated effectiveness metrics. Executive leadership can now balance and trade-off efficiency measures like throughput with effectiveness measures like profitability.

The race to define, standardize, automate and improve processes started with Henry Ford’s assembly lines. This ‘structuring’ of processes has driven tremendous productivity benefits for organizations, and managers are therefore trained to consider the organization to be a sum total of its processes. A key management goal is to drive more structure into business processes with automation being the desired end-state. While most data-driven, transactional processes have been automated over the decades, there is a class of knowledge-driven processes that have eluded efforts to structure or automate them. These ‘unstructured’ processes are variously referred to as Manual Processes, Expert Processes, Case Management, Supervisory Functions and similar. And the goal has been to apply process-centric thinking to structure and automate these pesky but high-value-add processes. This is not working and a new decision-centric approach needs to be considered.

All systems are active participants in business decision making to some degree.

The logic coded into business systems allows a level of ‘smartness’, and this is then amplified by the decision logic used by users – resulting in smart business operations. So, if we want to make the entire business operations smarter, we need to focus on decision making across systems and their users. Business Decision Management seeks to explicitly define and improve operational decisions; and to automate them through targeted deployment of sophisticated decision management technologies like Business Rules Management, Advanced Analytics and Optimization.