DMN for Business Analysts: Decision Modeling made easy

In this lesson, you will learn how to use the Camunda Modeler optimally for modeling decision processes (DMN). You will learn how to choose between the web and desktop version, what the advantages of both versions are and how you can get started right away.

Learning content:

• Introduction to the Camunda Modeler

• Differences between the web and desktop versions

• Advantages of using the Camunda Modeler for DMN and BPMN

• First steps with the web version of DMN-js

• Preparation for practical application

In this lesson, you will learn about the basic concepts and structure of a Decision Requirements Diagram (DRD). You will learn how to visualize and link the different elements such as decisions, input data and knowledge sources. We will give you an overview of the most important components of a DRD and how you can use them effectively.

Learning content:

• Basics and meaning of a Decision Requirements Diagram (DRD)

• Structure and components of a DRD

• Visualization of decisions and their dependencies

• Use of input data and knowledge sources

In this lesson, you will learn about the importance and application of input data in the context of a decision model. We explain how input data influences decision-making and how to display it correctly in the Decision Requirements Diagram (DRD). Using a simple alarm clock example, you will learn how input data is visualized in a DRD.

Learning content:

• Definition and role of input data in DMN

• Importance of input data for decisions

• Representation of input data in the Decision Requirements Diagram (DRD)

• Introduction to information requirements

In this lesson, you will learn about the basic components and structure of decision tables in DMN. Decision tables are at the heart of decision modelling as they model and visualize the decision processes. We explain the header, the input and output data as well as the rules and annotations that make up a decision table.

Learning content:

• Basic structure of decision tables

• Meaning of the header and the hit policy

• Identification and specification of input and output data

• Structure and numbering of decision rules

• Use of annotations to improve comprehensibility and traceability

In this lesson, you will learn how to create a simple decision table in Camunda Modeler. Using a practical example, you will learn how to define input data and model a decision that determines discounts based on the customer segment. This introduction will help you to understand the basic functionality and structure of a decision table.

Learning content:

• Introduction to the Camunda Modeler

• Defining the type of decision as a decision table

• Definition of input and output data

• Overview of the structure and components of a decision table

• Preparation for the detailed modeling of decision rules

In this chapter, you will learn how to create and apply specific rules or conditions in a decision table. Using a practical example, we will go through the process step by step to understand how rules are defined and what results are derived from them. You will also have the opportunity to create rules yourself and apply what you have learned.

Learning content:

• Introduction to the structure and function of rules in decision tables

• Step-by-step instructions for creating a rule

• Definition of conditions (inputs) and actions (outputs)

• Practical exercise: Creation of additional rules for different customer segments

In this lesson, we will put what we learned in the previous session into practice and check the result. You will learn how to optimize existing rules to make decision logics more efficient. We will use the Camunda DMN Simulator to test and improve the functionality of our decision table.

Learning content:

• Review of the created decision table and rules

• Introduction to the Camunda DMN Simulator for validating decision tables

• Merging rules to optimize the decision logic

• Use of comments and annotations to document rules

Learn how to work more efficiently with predefined values in Camunda DMN. This function helps you to avoid typing errors, save time and ensure consistency. With practical examples and step-by-step instructions, you will quickly master this useful technique.

Learning content:

• Introduction to predefined values

• Creating and inserting predefined values

• Advantages of using predefined values

• Practical application in the Camunda Modeler

In this course, we dive deeper into the world of rule engines and explore advanced concepts such as error handling and fallback rules. We will learn how to deal with unpredictable inputs and develop robust logics that work reliably even in unexpected situations.

Learning content:

• Creation of fallback rules for unforeseen inputs

• Use of negations in rule logics

• Implementation of universal rules for all inputs

Learn more about the different hit policies in DMN and their importance for the design of decision tables. Use practical examples to learn how to select the right hit policy for your decision models and avoid mistakes.

Learning content:

• Meaning of the "Unique" hit policy

• Application of the "First" hit policy

• Differences between "Priority" and "First"

• Function of the "Any" hit policy

• Error prevention through suitable hit policies

In this lesson, you will learn about the different data types in Camunda Modeler. Understand why data types are important and how they help you create accurate and computable DMN models.

Learning content:

• Meaning and purpose of data types in DMN

• Difference between strings (character strings) and numeric data types

• Use of integer for whole numbers

• Use of double and number for decimal numbers

• Use of Boolean for truth values (True/False)

• Date and time types: Date, Time, Datetime

• Time durations with DayTimeDuration and YearMonthDuration

Get to know the advanced hit policies in DMN, which go far beyond the basic ones. Discover how Collect, Rule Order and other policies work and how they can help you model your decision-making processes.

Learning content:

• Introduction to the "Collect" hit policy

• Using "Collect Count" to determine the number of applicable rules

• Use of "Collect Sum" for the summation of outputs

• Meaning of the data types for the correct use of "Collect Sum"

• Use of "Collect Min" and "Collect Max" to determine the smallest and largest output respectively

• Use of "Rule Order" to output sorted outputs based on rule order

• Difference between "Rule Order" and "Output Order"

In this lesson, we extend our decision table with multiple input data to model more complex decision logic. Using an example, we show how customer segments and order values can be combined to calculate differentiated discounts.

Learning content:

• Adding and connecting additional input data in the diagram

• Creating new input columns in the decision table

• Defining data types for new inputs (e.g. double for order values)

• Modeling conditions and rules with multiple inputs

• Applying the COLLECT SUM hit policy for the aggregation of outputs

• Use of comparison operators for numerical inputs (e.g. >=, <=)

In this lesson, we expand our model by linking several decision tables. We show how the output of a decision can be used as input for a subsequent decision and demonstrate the application using a practical example of discount approval.

Learning content:

• Modeling and linking multiple decision tables

• Defining data types for linked decisions

• Using comparison operators in downstream decisions

• Application of the Hit-Policy First to prioritize rules

• Simulation and validation of complex, linked decision models

In this lesson, we look at the knowledge sources (KS) in DMN and their importance for decision-making. You will learn how knowledge sources are used as authorities or knowledge sources to make decisions transparent and comprehensible.

Learning content:

• Definition and representation of knowledge sources in DMN

• Function and purpose of knowledge sources

• Application and significance of knowledge sources in decision models

• Examples of knowledge sources (manuals, legal texts, expert knowledge)

• Advantages of using knowledge sources: traceability, compliance, consistency and documentation

In this lesson, we look at the Business Knowledge Model (BKM) in DMN and its role in decision modeling. You will learn how BKMs help to centrally manage recurring business rules and standardize decision-making processes. Although Camunda does not support this in detail, we will give you a theoretical overview and practical application tips.

Learning content:

• Definition and meaning of Business Knowledge Models (BKM)

• Example application: Credit approval taking recurring business rules into account

• Advantages of using BKMs: consistency, time savings, easy maintenance

• Integration of BKMs into a Decision Requirements Diagram (DRD)

• Connection of BKMs with decision tables

In this chapter, you will learn how to use input expressions in DMN to transform input values before they are used in decision tables. This technique makes it possible to transform input data into a form suitable for decision making.

Learning content:

• Definition and use of input expressions

• Transforming input values with FEEL functions

• Practical example of transforming a process ID

• Application of the substring function for extracting character strings

• Integration of transformed inputs in decision tables

In this chapter, you will learn what FEEL is and how you can use this expression language to make your DMN models even more effective. FEEL allows you to create complex sets of conditions, transform data and perform calculations directly in your decision models.

Learning content:

• Definition and purpose of FEEL

• Use of FEEL for the transformation of input data

• Performing calculations with FEEL

• Examples of FEEL functions for string manipulation and numerical processing

• Simple and complex applications of FEEL in decision models

In this compact overview of Business Process Model and Notation (BPMN), you will learn the basics of this graphical representation language for business processes. BPMN makes it possible to document complex processes clearly and comprehensibly and is used worldwide for the visualization, analysis and optimization of business processes. In the following chapters, you will gain a comprehensive insight into the main components of BPMN in order to promote a common understanding of processes and facilitate communication between different departments.

Learning content:

• Introduction to BPMN: meaning and areas of application

• Overview of the basic BPMN elements: events, tasks, gateways and sequence flows

• Events: start events, intermediate events and end events

• Tasks: Different types of tasks (manual tasks, user tasks, service tasks)

• Gateways: Decision points and their types (exclusive, parallel and inclusive gateways)

• Sequence flows: Representation of process logic and sequences

In this chapter, we look at the basics of BPMN and the different types of start events. Using a practical example, the credit application process, we will learn how start events signal the start of a process and how to select the appropriate type for different situations. You will learn how to model the process precisely and comprehensibly by choosing the right start event and labeling it.

Learning content:

• Introduction to BPMN and the importance of events

• Start events: definition and representation

• Different types of start events and their use

In this chapter, we focus on the activities in BPMN and their importance in the process flow. Using the loan approval process as an example, we learn how different tasks are modeled and what activity types there are. We show how to name activities correctly and connect them through sequence flows to represent a functioning process.

Learning content:

• Definition and representation of activities in BPMN

• Naming activities: conventions and best practices

• Specification of activity types

In this chapter, we will learn about the use of gateways in BPMN. We will see how gateways help to represent decisions and branches in a process and control which paths are taken depending on specific conditions. Using the loan approval process as an example, we will show how an exclusive gateway is used to make the process flexible and adaptable.

Learning content:

• Definition and representation of gateways in BPMN

• Function and application of exclusive gateways

• Modeling decisions and branches in the process

• Use of gateways to control alternative process paths

In this chapter, we add Intermediate Events and End Events to our loan approval process. We will learn how these events influence the process flow and what they do. It is particularly important to understand how intermediate events stop the process and under what conditions it continues.

Learning content:

• Definition and function of intermediate events in BPMN

• Application of timer and message catch intermediate events

• Implementation of a timer event for process delay

• Meaning and representation of end events

In this chapter, we look at the synergy effects and advantages that result from the combination of DMN (Decision Model and Notation) and BPMN (Business Process Model and Notation). We discuss how both notations work together to make business processes more efficient, flexible and comprehensible.

Learning content:

• Decoupling process logic and decision logic

• Embedding DMN decision models in BPMN diagrams

• Advantages of automation through the combination of BPMN and DMN

In this chapter, you will learn how to bring BPMN and DMN together to design complex business processes efficiently. I will show you how you can outsource decisions from the process diagram to DMN and set up business rule tasks to manage the decision logic centrally.

Learning content:

• Identify the point in the business process for decisions

• Inserting and configuring business rule tasks in the BPMN diagram

• Linking business rule tasks with DMN models