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2020/2021  MA-MMFUV1030U  Data-Driven Decision Making

English Title
Data-Driven Decision Making

Course information

Language English
Course ECTS 3 ECTS
Type Elective
Level Part Time Master
Duration One Semester
Start time of the course 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
Course coordinator
  • Arisa Shollo - Department of Digitalisation
  • Ioanna Constantiou - Department of Digitalisation
Main academic disciplines
  • Information technology
Teaching methods
  • Face-to-face teaching
Last updated on 30-06-2020

Relevant links

Learning objectives
The goal of this course is to relate our knowledge of how decisions are made to a number of techniques and strategies for improving decision making. This will enable participants to support and improve their own decision making as well as to understand the decision making of others. We view the decision maker as a socially, economically, historically, and materially situated human - who increasingly uses algorithms for decision making and struggles with unrealistic demands and therefore has developed (individually and socially) heuristics, habits, routines, practices, and conventions which sometimes lead to algorithm aversion.

By the end of the course, students will be able to reflect on the complexities of decision making in organizations, their own decision styles and personal dispositions. They will be able to make decisions more deliberately and systematically and will be able to use decision analysis techniques, intuition and group processes, integrate their values into their decisions.
  • Understand and apply models and mechanisms for decision making in strategic decisions
  • Identify unconscious biases when making decisions and solving problems and reflect on common decision making traps that lead to fallacious reasoning and unfavorable outcomes
  • Identify criteria for when to trust intuition and when to push for analysis and evidence-based decisions
  • Reflect on how to make strategic decisions involving multiple (and changing) goals and stakeholders
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.
Data-driven Decision Making:
Exam ECTS 3
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 5 pages
Assignment type Essay
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

In the current competitive business environment, decision makers need to make decisions quickly and effectively based on abundant data. Data-driven decision making refers to organizations systematically collecting and analyzing various types of data, including input, process, outcome and satisfaction data, to guide, inform and/or automate a range of decisions from operational to strategic decisions. Typical examples are recommender systems that drive product recommendation decisions, credit scoring that drive lending decisions, employee analytics that drive hiring or promotion decisions etc. Making decisions includes many considerations such as weighing risk, understanding the specific situation encountered, identifying available options as well as considering long-range implications for the organization.
This course is about understanding and applying data-driven decision making while taking into consideration the decision maker’s experience and expertise. By knowing how data-driven decisions are actually made the students can learn how various decision techniques and strategies improve the quality of decisions. Some of these techniques and strategies are founded on mathematical models or computer software like algorithmic decision making; others build on theories about awareness and mindfulness. The course presents a wide range of such techniques covering the different theoretical approaches to decision making.

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, excersizes and exam
Student workload
Lectures 20 hours
Preparation and exam 70 hours
Last updated on 30-06-2020