2024/2025 KAN-CSAAO1001U Big Data Commercial Strategies
English Title | |
Big Data Commercial Strategies |
Course information |
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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
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Course coordinator | |
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Last updated on 14-05-2024 |
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Learning objectives | ||||||||||||||||||||||||||
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Examination | ||||||||||||||||||||||||||
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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. |
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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. |
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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 | ||||||||||||||||||||||||||
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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. |