Four Principles of Decision Management Systems

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Decision management systems are an exciting class of information systems. Organizations have been building decision management systems for long enough to prove how well they work. Decision management systems have helped many organizations reach new levels of business performance - delivering always-on, customer-centric systems that maximize operational effectiveness. These organizations could not operate at the level they do without these systems.

Yet many organizations have few decision management systems in use today. Their systems remain hard to change and manage, with businesspeople and IT teams on opposite sides of an ongoing argument. Analytics are kept separate from operational systems, limiting the ability of the analytic team to apply their insights in day-to-day activities. And these systems aren't capable of learning, so they continue to behave the way they have always behaved even as markets, consumers and competitors change around them.

The opportunity for these organizations is clear. They can develop agile, analytic and adaptive decision management systems and change their organizations for the better. Furthermore, they don't need to wait for technology to come out of a research lab somewhere or adopt "bleeding edge" systems. The technology they need - business rules management systems, predictive analytic workbenches and optimization systems - is proven, mature and robust.

Decision management systems are a powerful tool for enhancing existing business processes and legacy systems. Often requiring only minor changes to existing infrastructure, decision management systems add value to what you already have. Bringing together business, IT and analytic professionals around a common purpose, decision management systems will change how your organizations see their information systems. Focusing on the four principles of decision management systems is the key:

1. Begin with the decision in mind. Most information systems have been developed and are continuing to be developed around business functions, business data or business processes. Each of these approaches has pros and cons, but they all share a common challenge - they assume either that people will make all the decisions involved in the functions and business processes being automated or that how these decisions are made can be fixed. To develop decision management systems, we must begin with the decision in mind.

2. Be transparent and agile. Most information systems in use today are opaque and hard to change. This makes for long change cycles and a lack of responsiveness. Extensive information technology projects must be planned, budgeted and executed to make changes to the behavior of a system. These characteristics are unacceptable in a decision management system. If the code is opaque, then it will not be possible to see how decisions have been made or to verify that these decisions were compliant. If the code cannot be understood by those who have business know-how or experience, then it is unlikely to be correct. Organizational decision-making changes constantly, so agility is also essential. Decision management systems must therefore be both transparent and agile.

3. Be predictive, not reactive. In recent years, organizations have spent heavily on technology for managing and using data. These investments have taken data that was once hidden in transactional systems and made it accessible and usable by the people making decisions in the organization. Users of these systems are making decisions based on this data, using what has happened in the past to guide how they will act in the future. These approaches will not work for decision management systems as there is no human to extrapolate from the historical data. Passing only historical data into a decision management system would be like driving with only the rearview mirror - every decision being made would be based on out-of-date and backward-looking data. Decision management systems need to be given extrapolations explicitly - they must be predictive, not reactive.

4. Test, learn and continually improve. Most information systems have a single approach to handling any decisions that have been embedded in them. Every transaction is treated the same way, with possible alternative approaches largely eliminated during design to find the "best" approach. Once this singular approach has been implemented, information systems continue to work the way they were originally designed until someone explicitly recodes them to behave differently. These systems accumulate large amounts of data that might show certain actions are more effective than others, but the system will continue with its programmed behavior regardless. This approach is not an effective way to develop decision management systems. A decision management system must test, learn and continually improve. 

This is an excerpt from James Taylor’s new book, “Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics,” published by IBM in October 2011. Download a free eSampler from and purchase the book from here. Enter coupon code TAYLOR4389 at step 3 of checkout to save 35 percent and get free U.S. Shipping. 

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