By now, it is obvious to you that business decisions are the cornerstones of business operation. They are not sharping the operations; they are -the operations- and that’s why more specific business decisions are called operational business decisions. Many types of technologies aim to ensure organizations’ business decisions are consistent and create quality outcomes. As we already established the differences between business processes and business decisions, so we leave Business Process Management (BPM) technology out as they are not the right tool to manage and automate business decisions. But what about decision automation and decision management; are they the same? What are the differences?
Before getting to the nitty-gritty of the subject, let’s establish one principal concept many professionals misunderstand it. “Decision Making” is different from “Decision Execution” when it comes to business decisions.
Above is a very high-level abstract view but it illustrates the concept. If you don’t execute a decision, you will not create the outcomes. With having this in mind, now let’s get to the subject.
The decision management is a practice that models the decision-making processes. The core of decision management is to model business decisions to establish clearly how organizations make specific decisions in different areas. The questions that a decision management answers are about the “management” of the business decisions, of course:
- What is a scope and context of a particular business decision?
- How is the business decision made?
- What are the dependencies of the decision? E.g. data, system, processes, and other business decisions.
- What drives the decision? Internal and external forces such as regulation, policies, market opportunity, customer demand etc.
- What are the impacts of the decision? Positive and negative impacts, both quantitative and qualitative.
- How do we measure the performance of the decision?
- How do we measure its drift?
And many other questions around ownership, versioning and, so on…
One way to ensure the quality of business decision is to model them precisely and get a clear picture about them and every dependency around them.
Decision Management is mostly related to “decision making” rather than “decision execution”. Sometimes, decision management platforms include some aspects of decision execution as well. Such as rule-based decisions by providing ways of modeling business rules as part of the decision modeling and executing them.
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One of the obvious ways to ensure the consistency and quality of any task, for that matter, is to automate them by using automation technology to prescribe (or train) a computer on how to accomplish a task. If that task happens to be about executing a decision model (defined in the decision management practice) that is called decision automation.
Decision Automation (i.e. executing a decision) may vary based on the types of the business decision. A business decision might be rule-driven, so a business rule management system (BRMS) or business rule engine (BRE) will be invoked for executing the rules and satisfying the decision. A decision might be data-driven decisions. It means you need to look at data, get a good sense of it, analyze it using different techniques and algorithms for predicting a value, classifying items into groups, identifying abnormalities and etc. then base your decisions on the data result of your analysis.
If this is a repetitive task, then you can build Machine Learning models that do the task, such as classification, abnormalities detection, etc., for you at scale. So in these scenarios, decision automation will execute a trained ML model to satisfy a decision. And last but not least, a decision might be computational, e.g. calculating a bill amount, calculating a risk threshold, and so on. In these cases, the decision automation executes the calculation and satisfies the decision.
Some decision automation platforms allow a way of modeling business decisions and then using the models for execution.
How we see it is; decision management and decision automation come together to satisfy the business objectives.
In reality, organizations need to manage their business decisions, automate the repetitive ones and monitor the execution, identify the drift to the business KPIs and scale their operation. To do that successfully, many tasks should be done before management and after decision automation to ensure business decisions are all optimized, customer-centric and situation-aware across all the business processes, systems, channels and etc.
This requires a governance framework that takes care of everything related to business decisions in organizations and ensures the alignment of business objectives, business decisions, and automation technology.
This gap is filled by the Decision-Centric Approach® that is a methodical approach for governing business decisions in organizations.
Last updated February 9th, 2023 at 01:37 pm, Published August 4th, 2022 at 01:37 pm
CEO and the founder of FlexRule – He is an expert in architecture, design, and implementation of operational decisions, business rules, and process automation. Created Decision-Centric Approach, a methodology that brings People, Data, Rules, and Processes together to automate operational business decisions.
This approach is recognized by Gartner as the missing link to provide business value to organizations.
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