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2021/2022  KAN-CSAMO1002U  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
  • Management
  • Marketing
  • Strategy
Teaching methods
  • Blended learning
Last updated on 21-06-2021

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 V's (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 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 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 socio-psychological underpinnings of managerial mindsets, including why they are so powerful and difficult to change and how different managerial mindsets leads to different strategic decisions.
  • Explain the theoretical distinction between technology and mindset determinism, and how the two might both contribute to and/or obstruct innovative ideas.
  • Identify and discuss why digitalization requires legal and ethical considerations.
  • Analyze and explain organizational theories on organizational challenges to the use of Big Data and different managerial mindsets (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 to address the exam question, 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 Group exam
Please note the rules in the Programme Regulations about identification of individual contributions.
Number of people in the group 2-3
Size of written product Max. 10 pages
Assignment type Written assignment
Duration 2 weeks to prepare
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
Course content, structure and pedagogical approach

Big and Small Data as well as new managerial mindsets have 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 clear alignment between their own mindset and customers mindsets (related to expectation of companies). 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 relevant contact points with stakeholders, including customers. The companies that typically fail are the ones that apply Big Data technology as a simple extension of their existing strategic orientation, i.e., managerial mindset, rather than rethinking their ‘way of thinking’ and their interaction with the marketplace.


Applying Big and Small Data demands both technologically savviness and changes to managers’ existing mindsets. Otherwise they risk ending up the scenario of being expensive old companies, because managers are stuck in old thought patterns and silo thinking. Thus, to fully benefit from this new technology, managers need to be open for new ways to perspectivize the market. To utilize data about the customers optimally, and make them part of the company’s DNA often requires radically new thinking. As such, disruptive innovations are quite dependent on both a disruptive mindset and disruptive technology. We look at how companies best deal with this challenge.  


Aim of the Course

The aim is that students appreciate how Big and Small Data as well managerial mindsets can be used to improve/transform both internal procedures (logistic, production etc.) and external market interaction in B2B and B2C settings.


The course prepares students to work conceptually with many types of data (e.g., structured/unstructured, big/small) 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/prevent 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 overcome these. The course 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.    


Course Progression

Big (and Small) Data Commercial Strategies is a foundation course in the study program.


Disclaimer: This course will not embark on the analytical side of (big) data analytics, but rather take a cognitive approach by emphasizing the importance of mindsets in the usage of data. The analytical part will be covered in ‘Business Intelligence and Customer Insight’ during the second semester.

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 aim 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
There will be in-class discussions about cases and exam-related questions. At the end of the course a Q&A session is planned.
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 21-06-2021