Decision management

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]

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.

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]

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]

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]

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]

Decision management is applied across various industries to automate operational decisions:^[1][2]

• Banking and Finance: Credit risk assessment, loan origination, real-time fraud detection, transaction approval.
• Insurance: Claims processing and adjudication, underwriting automation, premium calculation.
• Retail: Dynamic pricing, personalized marketing offers, inventory management, supply chain optimization.
• Healthcare: Treatment plan recommendations, patient triage, claims validation, resource scheduling.
• Telecommunications: Service eligibility determination, network routing optimization.
• Supply Chain Management: Logistics optimization, demand forecasting, improving collaboration and speed.

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]

• Business process management
• Business rules engine (BRE)
• Decision support system (DSS)
• Decision intelligence
• Expert system
• Predictive analytics
• Operations research