Decisions are used by organizations to manage and execute their coordinated, value-adding decision-making and are thereby among an organization’s most important assets. To be able to manage deci-sions and underlying business rules, Decision Management (DM) and Business Rules Management (BRM) are increasingly being applied at organisations. One of the latest developments related to the domain of DM and BRM is the introduction of the Decision Model and Notation (DMN) in September 2015 by the Object Management Group (OMG). The goal of this technical paper is to provide students with a case to practice the specification, verification, validation, deployment, execution, monitoring and governance of business rules in practice.
Since an increasing amount of business decision/logic management solutions are utilized, organizations search for guidance to design such solutions. An important aspect of such a solution is the ability to guard the quality of the specified or modified business decisions and underlying business logic to ensure logical soundness. This particular capability is referred to as verification. As an increasing amount of organizations adopt the new Decision Management and Notation (DMN) standard, introduced in September 2015, it is essential that organizations are able to guard the logical soundness of their business decisions and business logic with the help of certain verification capabilities. However, the current knowledge base regarding verification as a capability is not yet researched in relation to the new DMN standard. In this paper, we re-address and - present our earlier work on the identification of 28 verification capabilities applied by the Dutch government [1]. Yet, we extended the previous research with more detailed descriptions of the related literature, findings, and results, which provide a grounded basis from which further, empirical, research on verification capabilities with regards to business decisions and business logic can be explored.
Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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