09.11.2017

4 Reasons for Business Analysts to Model Decisions with DMN

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Why is it worthwhile for business analysts to model decisions with DMN? The OMG standard pursues a top-down approach, offering a consistent methodology, from the analytical definition of requirements to the operational implementation. We explain four reasons for decision modeling with DMN.

DMN Models Uncover How Value Is Created

The easiest way to get start with DMN decision modeling is via the graphical Decision Requirements Diagrams (DRD). The DMN standard defines some (few) basic elements with which any decision can be fully modeled. The basic elements are shown in the figure below in the palette to the left.

Subject matter experts, business analysts or even IT users create these graphical models via drag & drop in a graphical editor, for example ACTICO Modeler. In this way, they visualize systematically, how a decision is made, what data is needed, which systems and organizational units are involved and which sub-decisions have to be made in advance.

This creates a clear picture of added value in the company. Particularly exciting is the decision modeling in the team. Because not infrequently, teams come to completely new knowledge through the analytical approach. Already that is one reason to model decisions with DMN.

Business domain experts can visually elicit and document decisions.

DMN Models Are Easy to Understand

The Decision Requirements Diagram in the previous figure shows quite clearly: DMN models are easy to understand. The DMN standard aims at better integrating business users without IT skills into the definition, analysis and maintenance of decisions. Because these users usually have the business knowledge, for example about credit decisions, risk calculations or legal aspects.

The ease of understanding is advantageous from two perspectives. First, companies can directly engage knowledge carriers in the development of IT applications. Because the Decision Requirements Diagrams form the preliminary stage for the technical implementation and execution of decisions. Second, the transparent DMN models ensure compliance, because they clearly show how the company makes decisions. Whether age checks, anti-money laundering checks or investor protection regulations – all these regulatory aspects can be linked together with the business aspects in a DMN model.

DMN Introduces A Systematic Approach

When operational, repetitive decisions are not clearly defined, decision-making is more random. That is, risks are judged by feeling rather than methodology, and existing knowledge is tied to people rather than shared. The solution is not to create rich (ambiguous) text documents, but to use best practices.

The DMN standard is the result of best practices. Numerous companies with a wealth of experience are involved in the continuous development of the standard under the umbrella of the Object Management Group (OMG). As with Business Process Model and Notation (BPMN) before, DMN introduces a systematic approach to define and implement business decisions.

DMN Connects Strategies with Their Implementation

The DMN standard provides for two key concepts: the requirements level at which decision-making strategies are graphically determined (see the figure above) and the implementation level at which decision-making logic is defined. This top-down approach improves the decision quality, because first the optimization of the higher-level requirements document (Decision Requirements Diagram) takes place. Only then is the technical implementation made. For the latter, the DMN standard provides a Decision Logic concept and its own expression language FEEL (Friendly Enough Expression Language). In the simplest case, companies use DMN Decision Tables as shown in the following figure to define the decision logic.

Users can define the decision logic in a tabular form.

These are just four of the reasons to model decisions with DMN. But the real business value lies in the automated execution of the DMN models. Take a look at this blog post to learn more on decision automation and real-time decisioning.