Decision Management

What is decision management?

Decision management refers to using technology to define, execute, monitor, and tune automated business decision logic across multiple applications and processes. It involves capturing business rules and decision logic in a central repository, testing and simulating those rules, and integrating them into operational systems and processes to optimize and automate decision-making.

Decision management platforms, also known as business rules management systems (BRMS), provide the capabilities to author, store, test, and manage business rules that can be used to make automated decisions. These rules, consisting of "if-then" logic statements, can be chained together to model complex decision processes that consider multiple factors. The rules and decision logic can invoke internal or external data sources to generate insights that feed into automated decisions.

Types of decision management

Depending on the decision management tool used or the industry a company is in, one of the following types of decision management will be utilized.

• Rules-based decision management: This approach encodes business policies, regulations, and parameters into rules-based decision logic. It optimizes high-volume, repetitive operational decisions across processes like loan origination, claims processing, and customer onboarding. The decision logic provides a consistent way to automate day-to-day choices.
• Predictive decision management: This type applies predictive analytics and data science models to decision making. Organizations feed historical data into models that identify patterns and relationships. Those models can then be operationalized into apps and systems that leverage predictive intelligence to guide decisions like credit risk scoring and customized marketing offers.
• Adaptive decision management: These systems provide capabilities to monitor live decision performance, identify areas needing improvement, and then use algorithms to self-adjust and optimize the decision logic in real time. This enables more agility to changing business conditions. It's instrumental in regulated industries.

• Hybrid decision management: Given the complementary strengths of the different approaches, many organizations implement hybrid systems that layer rules, predictive models, and self-adjustment. For example, a credit risk model could feed into a rules engine containing credit policies for final loan approval decisions.

Benefits of using decision management

Decision management brings standardized, compliant, and optimized decision-making to businesses, such as financial institutions facing complex, high-risk environments. It is a valuable technology for managing regulatory, market, and financial risks.

• Faster product innovation: In banking and insurance, launching new products requires regulatory approvals. With decision management, organizations can rapidly configure new products and decision logic to meet compliance needs. This accelerates time-to-market for personalized, competitive offerings.
• Consistent customer experiences: Organizations can ensure customers receive consistent outcomes across channels by centralizing decision logic. A loan applicant, for instance, should face the same decision criteria via online applications, in-person, over the phone, etc. Decision management enables unified decision-making.
• Agility to changing conditions: In dynamic financial markets, decision flexibility is critical. Decision management allows institutions to rapidly modify decision logic in response to economic shifts, risk tolerance changes, and new competitive threats. This business agility helps financial firms stay adaptable.
• Improved risk management: By monitoring decision performance, institutions can identify high-risk areas and fine-tune logic to minimize losses. Adaptive decision-making enabled by decision management is key for financial risk management.

Impacts of using decision management

Here are some key impacts of using decision management in businesses:

• Automates repetitive decisions: Decision management platforms allow businesses to automate high-volume, repetitive operational decisions that follow clear rules or policies. This improves efficiency and reduces cost.
• Centralizes decision logic: Decision rules and business logic can be consolidated into a central decision repository rather than scattered across applications. This enables consistency and oversight.
• Provides actionable insights: Dashboards, reporting, and analytics within decision management solutions help identify patterns, monitor performance, and continuously improve decision-making.

Basic elements of decision management

A decision management system includes the following core components:

• Business rule repository: A central repository to store, categorize, and manage business rules. 
• Rule authoring interface: intuitive web or spreadsheet-based interface for writing business rules without coding that uses natural language and templates.
• Simulation engine: A tool to validate and test business rules before deployment to ensure they behave as expected.
• Decision logic runtime: Production environment to execute business rule logic and drive automated decisions. Integrates with operational systems.
• Analytics dashboard: Reports, metrics, and visual analytics providing insights into rule usage, decisions, and performance.
• Admin console: Administration portal to manage rules, models, rule testing, runtime configuration, system access, and permissions.

Decision management best practices

To make decision management work, follow these best practices:

• Define governance clearly: Establish clear governance for decision logic ownership, testing, approval workflows, and production monitoring. This ensures accountability and consistency. Appoint decision stewards for each business domain.
• Start small, iterate quickly: Don't boil the ocean. Identify a few high-impact decisions and start building business rules. Test and refine iteratively. Once the process matures, expand to other decision areas.
• Test decisions extensively: Leverage testing tools to simulate various real-world scenarios and edge cases. Fix logic errors before deploying to production.

Decision management vs. business process management (BPM)

Here is a comparison between decision management and business process management (BPM):

• Focus: Decision management optimizes and automates business rules and decision logic. BPM models, automates, and optimizes broader business processes and workflows.
• Purpose: Decision management aims to systematize and improve specific decisions. BPM looks at end-to-end processes with the goal of enterprise-wide optimization.
• Capabilities: Decision management capabilities include rules engines, simulation, repositories, and analytics. BPM provides modeling, automation, monitoring, optimization, and simulation capabilities.

Understand what is business logic and how it helps companies gain actionable insights from their database.