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2017/2018  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
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Mikkel Flyverbom - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Communication
  • Organization
Last updated on 24-02-2017

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • • Articulate central features of big data, including historical, regulatory, societal and technological developments
  • • Discuss and reflect on the potentials and challenges of relying on different types of data and algorithms in organizational settings
  • • Analyze the technical and conceptual issues associated with text and other forms of unstructured data
  • • Understand and contrast different theories and conceptualizations of big data and its ramifications in terms of ethics and governance
Examination
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-step 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 and structure

 

This course offers students the advanced theoretical and analytical skills needed to articulate, develop and reflect critically on data-driven strategies in organizations. The course discusses the digital transformations that have led to the growing availability of and reliance on digital data and advanced algorithms, and includes a focus on societal, economic, regulatory and political dimensions, institutional developments and technological innovations. On this backdrop, the course looks at a variety of platforms and contexts where big data can be put to use in organization and strategy. This more practice- and strategy-oriented part focuses on various types of digital data, analytical methods and technologies, and reflects on the practical, strategic and theoretical opportunities and challenges they create. Finally, the course reflects on more overarching questions about knowledge, ethics, power and governance raised by big data.

 

 

 

Teaching methods
The course combines lectures based on the course curriculum with guest lectures by big data professionals and practitioners. The course combines lectures, discussions, 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 given tutorials and pre-tests, and there will 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

 

Flyverbom & Madsen (2015) Sorting data out: unpacking big data value chains

and algorithmic knowledge production, in Gesellschaft der Daten, transcript verlag

 

Gillespie (2014) The Relevance of Algorithms, in Media Technologies, ed. Tarleton Gillespie, Pablo Boczkowski, and Kirsten Foot. Cambridge, MA: MIT Press.

 

Hansen & Flyverbom (2014). The politics of transparency and the calibration of knowledge in the age of the algorithmic turn. Organization

 

Hutchby, I. (2001). Technologies, Texts and Affordances. Sociology, Vol. 35, Issue 2, pp. 441-456

 

Jenkins (2014): A/B testing and the benefits of an experimentation culture, available at http://blogs.hbr.org/2014/02/ab-testing-and-the-benefits-of-an-experimentation-culture/

 

Kallinikos, J. (2013) The Allure of Big Data, Mercury, issue 3, pp. 40-43

 

Mayer-Schönberger, V. and Cukier, K. (2013). Big data: A Revolution That Will Transform How WeLive, Work, and Think. Boston : Houghton Mifflin Harcourt

 

O'Connor et al (2010) From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series Bo Pang and Lillian Lee (2008) Opinion Mining and Sentiment Analysis

 

Rubio, F., D., and Baert, P. (2012). (eds). The Politics of Knowledge. London: Routledge

 

Silver, Nate (2012) The Signal and the Noise, introduction, available online:

http://www.penguin.com/book/the-signal-and-the-noise-by-nate-silver/9781594204111

 

Treem, J. and Leonardi, P. (2012). Social Media Use in Organizations: Exploring the Affordances of Visibility, Editability, Persistence, and Association. Communication Yearbook, Vol. 36, pp. 143-189

 

Zuboff, Sh. (1985). Automate/Informate: The Two Faces of Intelligent Technology, OrganizationalDynamics; Autumn ’85, Vol. 14, Issue 2, pp. 4-18

 

Last updated on 24-02-2017