Decision management refers to the process of designing, building, and managing automated decision-making systems that support or replace human decision-making in organizations.[1] It integrates business rules, predictive analytics, and decision modeling to streamline and automate operational decisions.[1] These systems combine business rules and potentially machine learning to automate routine business decisions[1] and are typically embedded in business operations where large volumes of routine decisions are made, such as fraud detection, customer service routing, and claims processing.[1]
Decision management differs from decision support systems in that its primary focus is on automating operational decisions, rather than solely providing information to assist human decision-makers. It incorporates technologies designed for real-time decision-making with minimal human intervention.[2]
Historical background
The roots of decision management can be traced back to the expert systems and management science/operations research practices developed in the mid-20th century.[3] These early systems aimed to replicate human reasoning using predefined logic. As technology advanced, decision management evolved to incorporate data-driven analytics and visual analytics tools. For instance, the Decision Exploration Lab introduced visual analytics solutions to help understand and refine decision logic, streamlining business decision-making.[3] This historical context helps place current decision management strategies within their evolutionary framework.
Operational vs. strategic decisions
A key distinction within decision management is its focus on operational decisions rather than strategic decisions.[4] Operational decisions are typically:
Frequent and repeatable: They occur regularly within standard business processes.
Structured: They involve clear inputs, logic, and outputs.
Embedded: They are often integrated directly into business processes and systems.
Time-constrained: They frequently need to be made quickly, often in real-time.
Strategic decisions, in contrast, are generally unique, complex, less structured, and made less frequently by senior management. Decision management primarily targets the automation and improvement of high-volume operational decisions.[4]
Approaches and key components
Modern decision management systems integrate a combination of rule engines, data analytics, and increasingly, AI models.[5] These components help organizations formalize decision logic, improve the quality and speed of decisions, and enhance agility in response to changing business environments.
Key components include:
Business Rules Management Systems (BRMS): These systems allow organizations to define, deploy, execute, monitor, and maintain the logic behind operational decisions, often expressed as business rules.[2] They separate the decision logic from application code, enabling business users to manage rules more easily.
Predictive Analytics & Machine Learning: Predictive analytics uses historical data and statistical techniques to forecast future outcomes or identify patterns.[2] Machine learning, a subset of AI, enables systems to learn from data without being explicitly programmed, improving decision accuracy over time. These are used alongside business rules to inform and automate decisions.
Decision Modeling: This involves creating visual representations of decisions, clarifying the required inputs, logic, and knowledge sources.[4][6] Standards like the Decision Model and Notation (DMN) provide a common graphical language for modeling decisions, helping to bridge the gap between business analysis and technical implementation.[5] The Decision Model framework, as described by von Halle and Goldberg, provides a structured way to link business logic with technology implementation.[6]
Modern trends: AI and hybrid decision-making
Artificial Intelligence (AI) is increasingly integrated into decision management, leading to "AI-enhanced hybrid decision management".[5] AI technologies, particularly machine learning, enhance decision-making by enabling systems to:[7] * Learn from vast amounts of data.
Adapt to new information and changing patterns.
Handle complex, unstructured data to uncover previously inaccessible insights.
Improve the accuracy of predictions used in decision logic.
Automate more complex aspects of decision-making, potentially augmenting human expertise.
Combining AI with established decision modeling standards like DMN facilitates the creation of more sophisticated, dynamic, and context-aware automated decision systems.[5]
Benefits and business drivers
Organizations adopt decision management to achieve several benefits:
Increased Efficiency and Speed: Automating routine decisions significantly speeds up processes and reduces manual effort.[8]
Improved Consistency and Accuracy: Automated systems apply decision logic consistently, reducing errors and variability.[2]
Enhanced Agility: Separating decision logic allows businesses to adapt rules and strategies quickly in response to market changes or new regulations, often without requiring extensive code changes.[8]
Regulatory Compliance: Decision management helps ensure that decisions consistently adhere to regulatory requirements through traceable logic.
Cost Reduction: Automation reduces the operational costs associated with manual decision-making.
Chief Information Officers (CIOs) often drive adoption to overcome challenges associated with outdated or hard-coded rule engines and to empower business users to manage their own decision logic.[8]
Real-world applications
Decision management is applied across various industries to automate operational decisions:[1][2]
Decision management systems frequently utilize a service-oriented architecture where decision logic is encapsulated within distinct "decision services". This architectural pattern, often aligned with frameworks like The Decision Model,[6] advocates for decoupling the business decision logic from the core business processes and application code. This separation enhances maintainability, scalability, and the reusability of decision logic across different applications.[6]
1 2 3 4 5 Taylor, J. (2011). Decision management systems: A practical guide to using business rules and predictive analytics. IBM Press. ISBN978-0-13-288438-9.
1 2 Broeksema, B.; Baudel, T.; Telea, A.; Crisafulli, P. (2013). "Decision exploration lab: A visual analytics solution for decision management". IEEE Transactions on Visualization and Computer Graphics. 19 (12): 1972–1981. doi:10.1109/TVCG.2013.130. PMID24051802.
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