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2024/2025  KAN-CSAAO1001U  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 cand.merc. and SAM
Course coordinator
  • Torsten Ringberg - Department of Marketing (Marketing)
Main academic disciplines
  • Information technology
  • Marketing
  • Strategy
Teaching methods
  • Face-to-face teaching
Last updated on 14-05-2024

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 to business insights.
  • Identify and explain how structured/unstructured data, including internet of things (IoT) might improve internal processes, logistics, and supply chain as well as insights into 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 its customers/clients.
  • 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, crowdsourcing, CRM, etc.).
  • Explain the usefulness of insights from neuro-science methods (EEG, eye-tracking, fMRI) and the senses (e.g., embodied cognitions), and what type of consumer insights each method provides.
  • Explain Deep (Small) Qualitative Data and their usefulnes in idenifying deep semi-subconscious drivers in B2B and B2C settings.
  • Explain the socio-psychological underpinnings of managerial mindsets (i.e., mental models and emboied cognition), including why mindsets are so influential as well as difficult to change, and how different managerial mindsets lead to different use of digital technologies, strategic orientations and decisions.
  • Explain the theoretical distinction between technology and mindset determinism, and how the two domains might both contribute to and/or obstruct innovative ideas.
  • Identify and discuss legal and ethical considerations related to the collection and use of data.
  • 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 clear and coherent arguments for your selection of key theories and models to address the exam question, and follow academic conventions in your written presentation.
Examination
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 3-4
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Spring
Make-up exam/re-exam
Same examination form as the ordinary exam
Same Examination form as the ordinary exam, but with 10 page individually written product.
Course content, structure and pedagogical approach

Big Data and AI 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, less than a one-third of companies that invest in Big Data and AI technologies benefit financially from it. In fact, studies show that companies that benefit the most have a clear alignment between their strategic mindset and customers' mindsets (related to their expectations of companies). The latter 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 use Big Data and AI technologies as simple extensions of their existing strategic orientation rather than exploring new available opportunities to engage with the market.Otherwise, they risk ending up being expensive old companies, because managers are stuck in old thought patterns and silo thinking. In addition, we look at how deep qualitative data (Small Data) also might help innovate companies' existing strategic mindsets

 

Thus, to fully benefit from new digital technologies, managers need both to appreciate what type of data might be useful and what the market prefer in terms of type of engagement. To attain disruptive innovations companies typicaly need to embrace both a disruptive strategic mindset and use new disruptive technologies. We look at how companies best deal with this challenge.  

 

Aim of the Course

The aim is for students to appreciate how Big and Small Data as well as managerial mindsets can be used to improve/transform both internal procedures (logistics, 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 digital transformation/maturity and how to overcome these. The course prepares students 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 conceptual approach to understanding mindsets, the use of data, and new digital technologies. A more analytical focus will be part of the ‘Business Intelligence and Customer Insight’ course during the second semester.

Description of the teaching methods
The course includes online material, industry speakers (when possible), 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 included.
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

Last updated on 14-05-2024