2023/2024
MA-MMBDV1055U Data-Driven Decision Making
English Title |
Data-Driven Decision
Making |
|
Language |
English |
Course ECTS |
3 ECTS |
Type |
Elective |
Level |
Part Time Master |
Duration |
One Semester |
Start time of the course |
Autumn |
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 (DIGI)
- Ioanna Constantiou - Department of Digitalisation
(DIGI)
|
Main academic
disciplines |
|
Teaching
methods |
|
Last updated on
03-07-2023
|
Learning objectives |
- Demonstrate how the course models and mechanisms for decision
making can be used to analyse strategic decisions in your own
organization or other chosen empirical context.
- Have the competence to recognize unconscious biases when making
decisions and solving problems and reflect on common decision
making traps that lead to fallacious reasoning and unfavorable
outcomes.
- Have the ability to 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. |
Examination |
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 |
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 |
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 |
21 hours |
Preparation and Exam |
54 hours |
|
Last updated on
03-07-2023