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2020/2021  KAN-CCMVV4042U  Datafication – foundations, transformations and challenges

English Title
Datafication – foundations, transformations and challenges

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 170
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Nanna Bonde Thylstrup - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Communication
  • Organisation
Teaching methods
  • Blended learning
Last updated on 04-06-2020

Relevant links

Learning objectives
At the end of the course, students should be able to:
  • Identify legal, ethical and political issues regarding a concrete datafication problem in an organizational context, making use of good searching principles and techniques.
  • Evaluate information sources on datafication, distinguishing scholarly sources from other content and critically assessing information from internet and other sources.
  • Manage information on datafication using online and other resources as well as appropriate citing and referencing techniques
  • Construct coherent and persuasive arguments in writing on current issues related to datafication, structuring the arguments logically and supporting them with relevant evidence
  • Plan and deliver an engaging and well-argued written presentation that coherently address both research question and audience.
Course prerequisites
Datafication - Foundations, Transformations and Challenges:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Written assignment
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
* if the student fails the ordinary exam the course coordinator chooses whether the student will have to hand in a revised product for the re- take or a new project.
Course content, structure and pedagogical approach

We are living through a time of fundamental digital transformations. The same innovative technologies and sociotechnical practices that are transforming society – enabling novel modes of interaction, new opportunities for knowledge, and disruptive business paradigms – can be abused to invade people’s privacy, provide new tools of discrimination, and harm individuals and communities. This course offers students the advanced theoretical and analytical skills needed to articulate, develop and understand the social implications of these digital transformations.


To provide frameworks that can help students develop, manage and reflect on data-driven strategies in organizations, this course will identify key social, legal and ethical issues at the intersection of technology and society. The course will provide and encourage learning that is grounded in advanced scholarship, informed and evidence-based public debates and real-life examples. The course will enable students to reflect and understand key issues in current digital transformations as well as anticipate future issues that may arise as a consequence of datafication.


No single theoretical framework can capture the complexity of data-centric technologies and their social and organizational implications. Cross-disciplinary insights are therefore needed for a range of analyses and management positions across private, public and non-profit organizations. Hence, the course brings together different perspectives, research methods, and practices from sociology, law, communication and data science. It furthermore includes perspectives from key stakeholders in digital transformations, namely researchers, entrepreneurs, activists, policy creators, journalists, and public intellectuals.


The course will focus on four key societal domains: rights and ethics, labor and automation, bias and inclusion and safety and critical infrastructure. These domains involve thinking through issues such as: how do data-centric technologies impact basic rights, and legal and ethical frameworks? How can responsible strategies be developed for automation and datafication of the workplace? What are algorithmic biases and how can we account for and counter them? And which strategies exist for safe and responsible integration of datafication and AI in core societal infrastructures such as hospitals, power grids and the police? Students will be taught these key issues and understand how they are related to the collection, organization, governance and discarding of data in public and private organizations. The course will include real-world cases, which will enable the students to contextualize and situate relevant case-based examples within existing scientific debates concerning data and society. 


The course is organized into three blocs. First, we begin with an introduction to the issue of data and society bridging historical knowledge, pioneer research and practitioner landscapes. This is followed by a bloc that explores key datafication domains: rights and ethics, labor and automation, bias and inclusion and safety and critical infrastructure. And finally, the course will discuss how public and private organizations can develop responsible social technologies. 



Description of the teaching methods
The course combines academic lectures, guest lectures by datafication practitioners, discussions, lab work, student presentations, and case studies in an engaging and participatory learning environment.
Feedback during the teaching period
Feedback takes a number of shapes in the course. We will discuss ideas for term papers throughout the course, students will be offered individual feedback and there will also be opportunities for students to engage in peer feedback.
Student workload
Lectures 33 hours
Exercises 20 hours
Preparations, readings and cases 90 hours
Exam assignment preparations 60 hours
Further Information

This course is a part of a minor in Data in Business

Expected literature

McIlwain, Charlton D. 2020. Black software: the Internet and racial justice, from the AfroNet to Black Lives Matter. Oxford University Press


Zuboff, Shoshana. 2019. The age of surveillance capitalism: the fight for a human future at the new frontier of power. London: Profile Books. 


Jørgensen, Rikke Frank, and David Kaye. 2019. Human rights in the age of platforms. http:/​/​mitpress.mit.edu/​9780262039055


Arora, Payal. 2019. The next billion users: digital life beyond the West. Harvard University Press 


Mikkel Flyverbom, Ronald Deibert and Dirk Matten. 2019. The Governance of Digital Technology, Big Data, and the Internet : New Roles and Responsibilities for Business, Business & Society, Vol. 58, No. 1, 1, p. 3-19


Noble, Safiya Umoja. 2018. Algorithms of oppression :data discrimination in the age of Google. NYU Press. 


danah boyd, Emily F. Keller, Bonnie Tijerina. 2016. Supporting ethical data research: an exploratory study of emerging issues in big data and technical research. Data & Society. https:/​/​datasociety.net/​output/​supporting-ethical-data-research-an-exploratory-study-of-emerging-issues-in-big-data-and-technical-research/​. 


Mayer-Schönberger, Viktor, and Kenneth Cukier. 2013. Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.



Last updated on 04-06-2020