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2018/2019  KAN-CCUSO1002U  Big Data Commercial Strategies

English Title
Big Data Commercial Strategies

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Torsten Ringberg - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
Teaching methods
  • Blended learning
Last updated on 17-12-2018

Relevant links

Learning objectives
  • Explain the emergence of Big Data and what Big Data is (i.e., structured/unstructured) from the perspectives of the nine Vs (Viewpoint, Volume, Variety, Velocity, Veracity, Validity, Visually, Volatility, and Value) and how each V contributes uniquely to business insights.
  • Explain how structured/unstructured data, including internet of things (IoT) might improve logistics, supply chain, and strategic processes and thereby reduce costs for both the organization and customers/clients/patients.
  • Analyze and explain how structured/unstructured data might help improve strategic customer insights and thereby create value for both an organization and customers/clients/patients.
  • Explain how the strategic use of Big Data technology, analytics, and dashboard might lead to innovative and potentially disruptive ideas (including theories/models explaining what constitutes a disruptive strategy, platforms, co-creation etc.).
  • Explain the role of social media in sensing and connecting with consumers (including the theoretical underpinnings of sentiment analysis, crowd sourcing, CRM, etc.)
  • Explain Big Data insights generated from neuro-science methods (EEG, eye-tracking, fMRI) and experiments (e.g., embodied cognitions) and what type of consumer insights each approach provides.
  • Explain when Small (qualitative) Data is useful for business development, including the theoretical underpinning of consumer subconscious drivers.
  • Explain the theoretical underpinnings of managerial mindsets, including why they are so powerful and difficult to change and how different managerial mindset leads to different strategic decisions.
  • Explain the theoretical distinction between technology and mindset determinism, and how the two types might both contribute to and/or obstruct innovative ideas.
  • Identify and discuss the legal, moral and ethical issues facing companies that rely on Big Data, including why digitalization requires legal and ethical considerations.
  • Analyze and explain organizational theories on organizational challenges to the use of Big Data (e.g., resistance, resources, incentive structure, skillsets, buy-in, motivational issues), and why organizations are located on various paths toward achieving digital maturity, and how to overcome these challenges.
  • Present a clear and coherent argument for your selection of key theories and models and follow academic conventions in your written presentation.
Big Data Commercial Strategies:
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 2 weeks to prepare
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
Course content and structure

Big (and Small) Data has the potential to revolutionize the art of management and marketing. Yet, in spite of its high operational and strategic impacts, still less than a one-third of companies that invest in Big Data technology benefit financially from it. Studies show that companies that benefit have a clearly expressed customer strategy and use data centrally as a key strategic tool. These companies have a strategic approach to collecting, processing, sharing and proactively using data both internally (i.e., logistically) and externally (i.e., value creation) across contact  points with stakeholders, including customers. The companies that typically fail are the ones that apply Big Data technology as a simple extension to their existing strategic orientation rather than rethinking their interaction with the marketplace and learn from new insights.

Big Data makes it possible to improve logistics and create relevant and meaningful customer experiences leading to an increase in the bottom line. Yet, applying Big Data demands both technologically savviness and changes to managers’ existing mindsets. Otherwise we end up with the scenario of expensive old companies, i.e., ‘old’ businesses with new technology, which were unable to generate the necessary impact, because managers are stuck in old thought patterns and silo thinking. Thus, to fully benefit from this new technology, managers need to have flexible mindsets. To utilize data about the customers optimally and make them part of the company’s DNA requires radically new thinking. As such, disruptive innovations are as dependent on a disruptive mindset as it is on a disruptive technology.  We look at how companies best deal with this challenge.  

Our aim is to follow the course schedule but at times we might change the flow slightly due to logistics of industry speakers, presentations, discussions, etc. We will cover the topics. 


Aim of the Course

Big Data (and Small) strategically both in terms of monitoring and improving internal procedures (logistic, production etc.) and understanding and satisfying consumer preferences, motivation and behavior based on actively monitoring their behavior and engaging with them via social media platforms.  The course also prepares students to work conceptually with many types of data (e.g., structured/unstructured) and to assess the usefulness of such data to address a given managerial task (e.g., logistics, consumer value, unique positioning). It provides the basis for developing insights into how managerial mindsets both enable/disable managers from identifying potential disruptive opportunities from data. Finally, it sensitizes students to identify organizational and structural challenges that slow down the digital transformation/maturity and how to begin to overcome these. The course also prepares students how to use sensitive personal data gathered across multiple touchpoints with consumers. The course provides students with an understanding of the complexities of Big (Small) Data Technology and that it is strategy and not technology that primarily drives digital transformation. It prepares the student for an exciting career in digital and strategic marketing. The course does not require any technical expertise in terms of an IT or data analytics background.    


 Touch points?

 I would not explicitly state this in the course description. You could emphasize it, for instance during the first lecture.

Description of the teaching methods
The course includes online material, industry speakers and lectures with in-class discussions and small workshops. It is expected that students participate actively.
We do not rely on blended learning as the field evolves so rapidly that invites industry speakers from companies that develop and/or use Big Data ensure more up-to-date insights. We also try to include the latest articles in both academic and managerial (e..g. HBR, MITSloan) journal outlets.
Under each Session you can see which of the learning objectives (i.e., 1, 2, 3…..12) that are covered.
Feedback during the teaching period
Student workload
Teaching 33 hours
Preparation 123 hours
Exam 50 hours
Expected literature

Indicative literature (more literature will be announced upon enrollment):


Required Book

Rydén, Ringberg, Østergaard-Jacobsen (2017) Disrupt your Mindset to Transform your Business with Big Data. Book includes an online mindset test.

Last updated on 17-12-2018