Kurs Systems Modeling for Interdisciplinary Contexts

Jennifer-Marieclaire Sturlese MSc (WU)

In interdisciplinary settings, collaboration often breaks down not due to a lack of expertise, but because disciplines differ in how they conceptualize, structure, and communicate complexity. This course addresses this challenge by positioning modelling as a shared visual and conceptual language that enables understanding across disciplinary, technical, and organizational boundaries.

The course introduces core concepts from data, business processes, and information systems design, not as isolated technical topics, but as communication instruments that support alignment among diverse stakeholders. Students learn how models can translate between perspectives and how they can be adapted to different audiences and purposes.

The course covers:

- Data modeling, including the distinction between data, information, and knowledge, typical challenges in data processing, and the role of databases in organizing and maintaining data. Students gain an overview of database management systems (DBMS), relational databases, and the basic idea of Structured Query Language (SQL), with a strong focus on conceptual understanding rather than technical depth. Data modeling using Entity Relationship Models (ERM) is introduced as a means to communicate data requirements clearly.

- Process modeling, including how organizations structure work through processes. Students explore the Business Process Management (BPM) life cycle and learn to model processes using Business Process Model and Notation (BPMN) as a way to create shared understanding between business, technical, and managerial stakeholders.

- Systems modeling and requirements engineering, including the life cycle of information systems, common development process models, and the role of requirements engineering in translating stakeholder needs into system specifications. The Unified Modeling Language (UML) is introduced as a standardized modeling language for representing system structure and behavior.

Learning outcomes: By the end of the course, students will be able to: 

- distinguish between data, information, and knowledge,

- understand the role of databases and data models in communicating data requirements,

- model data structures using ERM,

- analyze and model business processes using BPMN,

- understand the purpose of requirements engineering,

- apply basic UML models to communicate system requirements, and

- apply modeling techniques to effectively communicate with interdisciplinary audiences.