What is Decision Management?
Modern-day business operations are centered around processes and data. However, when knowledge and expertise are forced into hard-coded, inflexible systems, bottlenecks occur. By adopting and scaling the capabilities of business rules, decision modeling, and machine learning, Digital Decisioning leverages data and expertise to create business value, improve results, and deliver a great user experience to effectively meet today’s operational requirements. For Digital Decisioning to deliver true value, it must initially focus on solving business problems that will, ultimately, contribute to positive and desirable business outcomes. Technology, automation, and methodologies are essential for Digital Decisioning, but they are always at the service of business decisions.
The foundation for successful Digital Decisioning projects is applying a Decision Management approach. Decision Management models business decisions first. These decisions, for the most part, have to do with customers and how you engage with them, deliver services to them, or handle their transactions.Once you have a good grasp of what decisions your business needs to make and how these decisions will advance your business, Decision Management shows which technology and analytics tools you need to invest in and how to integrate them. Finally, Decision Management focuses on the processes and infrastructure needed to ensure continuous improvement and business engagement. To maximize success, adopt a proven approach such as the DecisionsFirst™ approach from Decision Management Solutions.
Adaptive Control is about continuously improving the way you make decisions. Some of these changes will come from changing business conditions that force a change in the approach being taken to the decision. Mostly, however, it is a case of making a decision better and better over time to boost profits, reduce losses, or improve retention.
You constantly learn more about your customers and gather more information about their behavior. New insights and market trends come from you, your competitors and from third parties. A process for continual review and improvement of how you make a decision allows you to detect and respond to changes in the behavior of your customers without having to start a special project and helps you show an ROI for the data you collect and analyze.
At the point of decision it is not known what the long-term outcome of that decision will be. Therefore:
- If you use a single approach for every decision then you will only plot one of these curves and will have no data about how other actions might have resulted in better (or worse) results.
- It is important to track your results and know what kind of impact you are looking for – short term or long term? Low risk or high risk?
This is what makes Adaptive Control so important.
Champion/Challenger is an important concept in adaptive control. Your current or preferred approach is a “Champion”. “Challenger” approaches are then developed. Each Challenger differs from the Champion in some measurable and defined way. Perhaps it has different business rules, perhaps it uses a different risk model, perhaps it is more aggressive about retaining customers. Each Challenger will therefore deliver different results from the Champion.
These results may be better or worse but only testing the approaches with real transactions, in a live environment, can really show. A Decision Service is therefore configured to push a small percentage of the transactions through each of the Challenger approaches while pushing the majority through the Champion. Results from the different approaches can be compared and measured over time. If a Challenger does better than the Champion, it can be made the new Champion and the process of identifying and testing new Challengers repeated to continually improve the decision.
- The production environment must allow a Decision Service to have not just its current or Champion implementation but also a number of potentially better strategies – Challengers.
- It must be possible to randomly take some of the transactions and run them not through the rules and analytics that are currently defined as the Champion but through one of the Challenger strategies.
- You need to capture the results in a way that allows you to know which decisions were taken in which way (Champion v Challenger) when you come to do analysis.
- A simulation/testing environment must be available. Within this simulation environment, you need to be able to do some testing of how different rules and analytics might affect results in various “what-if” scenarios.
- Business users must be able to analyze the results both of production Challengers and of simulations.