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2026/2027  BA-BMAKO6005U  Digital Data Analytics

English Title
Digital Data Analytics

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Bachelor
Duration One Quarter
Start time of the course Third Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for Service and Markets
Programme BSc in Business Administration and Market Dynamics and Cultural Analysis
Course coordinator
  • Constant Pieters - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
  • Methodology and philosophy of science
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 29-05-2026

Relevant links
Learning objectives
Students that have successfully completed this course have demonstrated that they can:
  • Identify a relevant business problem that is derived from a specific case-analysis
  • Source a valid digital dataset that promises insights in a specific business problem
  • Execute meaningful (statistical) analyses of digital data
  • Develop relevant managerial recommendations based on digital data analysis results
  • Conduct a research project that centers around an analysis of digital data and that follows academic standards
  • Reflect critically on strengths and weaknesses of a digital data analytics research project and its philosophy of science
Examination
Digital Data Analytics:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 3-5
Size of written product Max. 15 pages
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration
Written product to be submitted on specified date and time.
10 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer and Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Students that participate in the re-exam should complete a new research project (i.e., a new problem statement and a new digital data analysis) that has the same requirements as the regular exam has.
Description of the exam procedure

Students work on the exam in groups throughout the course. Groups work on a digital data analytics research project. The project focuses on a case that is assigned at the start of the course.

 

At the end of the course, groups hand in a written group product.

 

The oral exam is based on the written group product but covers all course material, which includes the course literature. Groups start the exam with a brief presentation. Groups also receive the opportunity to briefly point out any unclarities in the research report and to rectify any mistakes. The remainder of the oral exam consists of an academic discussion of the written product, but the examiners can also ask students to reflect on the course material more generally.
Course content, structure and pedagogical approach

Digital datasets contain digital traces of how certain stakeholders interact with a business model. These data therefore promise unique insights into markets, companies, and consumers. Digital data and other types of secondary data are often publicly available but come with a specific set of challenges. Specifically, digital datasets capture novel phenomena and are large, complex, and multifaceted, such that existing theories often provide insufficient guidance on how to analyze these data and interpret the results. Proficiency in digital data analytics—to source valid digital data, to successfully conduct meaningful analyses of these data, and to derive actionable managerial recommendations from the results—is therefore invaluable for academics and practitioners alike.

 

To develop students' digital data analytics skills, this course combines business research methods, philosophy of science, business model analytics, quantitative data analytics, and practical perspectives. Students work on a digital data analytics research project that is centered around a real-world business model. Three course phases train students in how to identify a research opportunity, explore its terrain, and advance understanding. Overall, this course aims to develop the skills required to successfully complete a research project that focuses on digital data analytics. Furthermore, the competencies gained in this course are directly relevant to students' other projects, including the bachelor project.

 

The course syllabus, distributed at the start of the course, provides additional details on the course topics, the course structure, and the course schedule.
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
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Activities that contribute to new or existing research projects
Description of the teaching methods
Lectures present students contemporary frameworks and tools to develop a research project based on digital data. Students prepare by reading assigned literature, which is then reflected on in class. Students then apply the relevant concepts to class exercises that focus on an example case.

Exercise classes provide room for application, reflection, and guidance.

Groups are expected to work independently on their projects between learning activities.
Feedback during the teaching period
Exercise classes provide groups with opportunities to receive feedback. Instructors and peers give feedback based on group work, presentations, and class discussions.
Student workload
In-class presence 38 hours
Preparation 40 hours
Groupwork 100 hours
Exam 28 hours
Expected literature

Books (selected chapters)

 

Heath, Chip and Karla Starr (2022), Making Numbers Count: The Art and Science of Communicating Numbers. New York: Avid Reader Press.

 

Osterwalder, Alexander and Yves Pigneur (2010), Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Hoboken, New Jersey: John Wiley & Sons.

 

Articles

 

Anvari, Farid, Rogier Kievit, Daniël Lakens, Charlotte R. Pennington, Andrew K. Przybylski, Leo Tiokhin, Brenton M. Wiernik, and Amy Orben (2023), "Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters," Perspectives on Psychological Science, 18 (2), 503-07.

 

Boegershausen, Johannes, Hannes Datta, Abhishek Borah, and Andrew T. Stephen (2022), "Fields of Gold: Scraping Web Data for Marketing Insights," Journal of Marketing, 86 (5), 1-20.

 

Chapman, Randall G. (1989), "Problem‐Definition in Marketing Research Studies," Journal of Consumer Marketing, 6 (2), 51-56.

 

Funder, David C. and Daniel J. Ozer (2019), "Evaluating Effect Size in Psychological Research: Sense and Nonsense," Advances in Methods and Practices in Psychological Science, 2 (2), 156-68.

 

Golder, Peter N., Marnik Dekimpe, Jake T. An, Harald J. Van Heerde, Darren S. U. Kim, and Joseph W. Alba (2023), "Learning from Data: An Empirics-First Approach to Relevant Knowledge Generation," Journal of Marketing, 87 (3), 319-36.

 

Lindgreen, Adam, C. Anthony Di Benedetto, Roderick J. Brodie, and Elina Jaakkola (2021), "How to Develop Great Conceptual Frameworks for Business-to-Business Marketing," Industrial Marketing Management, 94, A2-A10.

 

Steenkamp, Jan-Benedict E. M., Marc Fischer, Kelly L. Haws, Maura L. Scott, and Rebecca J. Slotegraaf (2026), "Cementing JM’s Impact on the Marketing Ecosystem: Empirical Execution," Journal of Marketing, 90 (3), 1-12.

 

Van Heerde, Harald J., Christine Moorman, C. Page Moreau, and Robert W. Palmatier (2021), "Reality Check: Infusing Ecological Value into Academic Marketing Research," Journal of Marketing, 85 (2), 1-13.
Last updated on 29-05-2026