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2022/2023  KAN-CKOMO4002U  Digital Data Analytics and Consumer Foresight

English Title
Digital Data Analytics and Consumer Foresight

Course information

Language English
Course ECTS 15 ECTS
Type Mandatory (also offered as elective)
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Organizational Communication, MSc
Course coordinator
  • Steffen Blaschke - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Customer behaviour
  • Communication
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 04-10-2022

Relevant links

Learning objectives
  • Describe and explain the preconditions, theories, concepts, models, and methods introduced in the course
  • Apply these preconditions, theories, concepts, models, and methods to an analysis of specific issues in management as well as develop solutions to these issues
  • Provide an account of the theoretical and applied (practice-oriented) interconnections between management and data analysis
  • Reflectively consider (meta-theoretically and critically) the preconditions, theories, concepts, models, and methods introduced in the course, and the potential and limitations of these in theory and practice
Course prerequisites
Participation in this course requires experience with advanced statistics
Digital Data Analytics and Consumer Foresight:
Exam ECTS 15
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 20 pages
Assignment type Written assignment
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

The course combines theoretical perspectives from all areas of management with digital data analytics. It equips students with an advanced methodological skill set rooted in inferential and bayesian statistics, machine learning, and network analysis.


The point of departure in management always starts with a problem statement or research question. Typical problems include make-or-buy decisions, cost leadership or differentiation, market entry or exit, in- or outsourcing, mergers and acquisitions, joint ventures and strategic alliances. Digital data analytics provide methodological approaches to find solutions to these and other problems. Consider, for example, an airline that needs to decide on opening new flight routes. It may use digital data analytics and strategic foresight to look into and predict travel preferences in order to make an informed decision on market entry. Another example is a company that wants to leverage its foray into sustainable sourcing. It may use digital data analytics to inquire into stakeholder sentiments and build a communication strategy according to the prediction of future trends.


Digital data analytics not only require sound methodological understanding and practical programming skills but also the ability to identify and articulate ethical issues associated with data collection, analysis, and prediction. To cultivate their ethical sensibility and reflexivity, students will explore the ethical dilemmas of digital data analytics through active engagement with academic literature and relevant business cases.

Description of the teaching methods
Description of the teaching methods
Feedback during the teaching period
Student workload
Lectures and exercises 60 hours
Preparation for classes 352 hours
Total 412 hours
Last updated on 04-10-2022