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2026/2027  MA-MMBDV2608U  Data-Driven Decision Making

English Title
Data-Driven Decision Making

Course information
Language English
Course ECTS 5 ECTS
Type Elective
Level Part Time Master
Duration One Semester
Start time of the course Autumn, Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 10
Max. participants 30
Study board
Study Board for Master i forretningsudvikling
Programme Master of Business Development
Course coordinator
  • Arisa Shollo - Department of Digitalisation (DIGI)
  • Ioanna Constantiou - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
Teaching methods
  • Face-to-face teaching
Last updated on 01-06-2026

Relevant links
Learning objectives
After completing this course, participants will be able to:
  • Identify and explain a relevant decision-making challenge related to the integration of data and intuition within your own organization or another chosen empirical context
  • Demonstrate how the course’s concepts and theories on data-driven decision-making, intuition, bias, and stakeholder integration can be applied to analyse the chosen challenge
  • Evaluate and reflect on the theoretical and practical implications of combining data and judgment in decision-making, including limitations, trade-offs, and alternative approaches
Course prerequisites
The course targets different levels of managers, specialists, and analysts who are involved in organizational decision making. The course is also relevant for professionals who would like to understand the challenges and opportunities of data-driven decision making in organizational settings.
Examination
Data-Driven Decision Making:
Exam ECTS 5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 5 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

In today’s fast-paced business environment, the ability to make informed, data-driven decisions is essential for executives. This course equips participants with the skills to effectively combine data analytics with intuitive judgment, enhancing their decision-making capabilities in complex, dynamic situations. By enrolling in this course, executives will gain a competitive edge by learning how to balance evidence-based strategies with their professional expertise, ultimately leading to better outcomes for their organizations.


In particular, participants will explore the theoretical foundations of decision-making, from rational and evidence-based approaches to the role of intuition and expertise. The course emphasizes practical application through case studies and interactive exercises, enabling participants to apply these concepts directly to their own organizational contexts. A particularly interesting aspect of the course is the exploration of how machine learning and AI are reshaping decision-making structures, and how executives can leverage these technologies while maintaining their unique human judgment.
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • Teacher’s own research
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
Description of the teaching methods
Case studies, lectures presenting readings, guest lecturers from industry and active student involvement in discussions and reflections. Teaching is based on that students have read teaching material prior to class.
Feedback during the teaching period
Feedback will be given during lessons, exercise and exam.
Student workload
Lectures 21 hours
Preparation and Exam 54 hours
Last updated on 01-06-2026