Data Driven Business Model Development (BDS300)

This course is a follow up of BDS250 Big Data Analyses where the focus is an applied approach into a set of businesscases.An increasing number of companies, both within private and public sector, base their operations on digital businessmodels. These models give a constantly stream of incoming data about e.g., internal quality, cost and profit margins,customers’ behaviors and preferences, customers’ lifelong value etc. Such data will improve decisions about externadaption and intern integrations. However, the course will also study adaptive management processes that are needed in order implement decision systems and the realization of actual decisions. The course applies anincremental view of adaptive processes.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

BDS300

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English, Norwegian

Offered by
Time table

View course schedule

Note

Course does not start before autumn 2025

Content

  • Identifying and classifying incoming data streams from digital business operations within different servicebusiness cases.
  • Alternative decision models based on continuously incoming digital data.
  • How to implement digital decision models into actual business organizations.
  • How to design an adoptive management system that produce incremental changes in service deliveries and internal quality.

Learning outcome

The candidates will upon completion of the course have the following learning outcome defined in knowledge, skills and general competence:

Knowledge

  • have knowledge of central issues and challenges in designing data driven decision models and adoptiveincremental changes in contemporary digitalized business operations
  • have knowledge of how central stakeholder groups can participate, be included and affected by continuousincremental changes.

Skills

  • be able to recognize and categorize digital data feedback from digitalized operations.
  • be able to quality check and analyze incoming data.
  • be able to suggest alternative adaptive decision models, some of their advantages and disadvantages
  • .be able to identify terms and conditions from a stakeholder perspective when it comes to implement anadaptive system that produce incremental changes
  • .be able to recognize effects of digitalization from a stakeholder’ perspectives.

General competence

  • be able to reflect around ethical perspectives related to adaptive data-based decision models.
  • be able to apply course perspectives in new areas and situations related to change processes in general.
  • be able to communicate and engage in discourses about research-based theories and analyses in the area ofdigitalized business environments.

Required prerequisite knowledge

None

Exam

Case analyze papers and oral exam

Form of assessment Weight Duration Marks Aid
Case analyze paper 1 15/100 Letter grades
Case analyze paper 2 15/100 Letter grades
Case analyze paper 3 15/100 Letter grades
Case analyze paper 4 15/100 Letter grades
Oral exam 40/100 Letter grades

Course teacher(s)

Course coordinator:

Sigbjørn Barlaup Pedersen

Method of work

Active case discussions in class, guest lectures from digitalized businesses, business site visits.

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto