The process of implementing a decision engine
Automation of decision-making using a decision engine is a process that companies start when they want to optimize. The reasons for optimization are usually: reducing costs, improving efficiency and speed of business processes, or making decision-making scalable.
The process of automating decision-making using a decision engine usually starts with the identification of a business process where a decision is made. The next step is to understand how the decision is made and what data is used in the decision process. After that, a decision model is created which is then translated into a decision engine.
Initiating decision automation via a decision engine often serves as a cornerstone for companies looking to optimize operations. Common drivers for such optimization include cost reduction, acceleration of business processes, and making decision-making scalable.
The journey towards decision automation generally commences with the identification of a critical business process that requires decision-making. The subsequent steps involve understanding the existing decision-making process, the data that informs it, and then translating these elements into a formal decision model to be implemented in a decision engine.
Formalizing Decision Strategy
The initial phase in deploying a decision engine involves scrutinizing the existing decision-making mechanisms. Selection of a suitable decision engine should only be made post this assessment and subsequent formalization.
Upon formalizing the decision strategy, one can then proceed to evaluate different decision engines to determine their suitability in meeting the specific requisites of the formalized strategy.
While hiring external consultants is an option, performing an internal review of your decision-making logic can be beneficial. This internal scrutiny may unearth crucial know-how that can serve as a core competency for your business and that you may not want to risk being shared with competitors.
The outcome of this strategy review should be a formal document outlining the rules, decision logic, and data requirements, along with methods for data collection.
This document should act as the blueprint for implementing your decision engine. The choice of the decision engine should align with the functionalities stipulated in the formal document. If, for example, your decision logic requires real-time data enrichment from external sources or depends on predictive analytics, your chosen decision engine should be equipped to handle these requirements.
System Integration with Decision Engine
The ultimate goal of decision automation is to eliminate human intervention in the decision-making processes of an information system.
As such, the decision engine needs to be tightly integrated with the core systems where it will execute automated decision-making.
The integration phase should emphasize well-defined data pipelines to ensure the decision engine receives accurate and complete information. Planning for future changes, such as replacing the decision engine, is also vital.
Resilience and Business Continuity
Ensure that your integration is designed with resilience in mind, providing a fallback mechanism in case of decision engine failures, to maintain business continuity.
Two prevalent approaches exist for handling such technical disruptions: one is to define a default decision strategy within the core system, and the other is to use message queues to temporarily store decision requests for later processing.
The choice between these approaches will hinge on business-specific criteria, such as the time-sensitivity and criticality of the decisions involved.
Data Preparation Guidelines
- Assemble all evaluation-relevant data
- Append system-generated technical attributes
- Embed contextual details within the data payload
To align with the specified decision strategy, the core system must generate a data payload that includes all requisite attributes.
Data Format Standards
This data payload should be formatted in a universally accepted standard, such as JSON, to ensure that the decision engine can easily ingest and interpret the data.
Technical Attributes
Beyond the core data set, include system-generated attributes like random numbers for Champion-Challenger testing and additional meta-data like timestamp and origin system details.
Contextual Details
Include relevant context to enhance decision accuracy. Such context could be temporal (time of day, for example), environmental, or user-specific factors, among others.
Implementing Strategy within the Decision Engine
Once the decision strategy has been formalized in a document, the next step is its implementation within the decision engine, usually through a rule management UI.
Internal Validation of Decision Strategy
Prior to deployment, conduct internal reviews to validate the defined strategy's efficacy, ensuring that it operates as intended when integrated into the production environment.
Post-Implementation Decision Audits
A critical component of decision engine management is auditing historical decisions and contrasting them with the engine's evaluations.
If discrepancies exist, halt the automation transition until the root cause of the differences is fully understood.
Staging and Testing
Each new or revised decision strategy should be rigorously tested in an isolated, controlled environment before live deployment.
This testing sandbox should facilitate the execution of a wide range of scenarios with reproducible outcomes.
Real-time Monitoring and Feedback Mechanisms
Post-integration, it's crucial to continuously monitor the quality and business impact of the decisions rendered by the engine.
Monitoring should zero in on decision accuracy and resultant business metrics. Establish a feedback loop to capture and analyze the real-world outcomes of these decisions.
This data is invaluable for ongoing engine calibration and rule adjustment to improve decision quality.
Deployment Documentation and Accountability
It's a best practice to maintain a formal changelog of decision strategies deployed to production. In regulated sectors like finance, formal approvals from department heads might be obligatory.
This log enables post-mortem analysis of decision outcomes, particularly useful in scenarios where there's a significant time lag between decision-making and observable outcomes (e.g., loan underwriting and default rates).
Conclusion
Decision engines are potent assets in improving customer interactions, combating fraud, and optimizing operations.
The preliminary step is to clearly articulate and formalize the decision strategy, followed by its meticulous translation into the decision engine's rule set.
Subsequent system integration must adhere to a well-defined procedure to circumvent future complications.
Once deployed, ongoing vigilance is required to ensure that the engine’s decisions align with organizational objectives and KPIs. Mechanisms should be in place for systematic testing and adjustments whenever strategy modifications are deployed.
