Data-Driven Business Modelling and Strategy

2023/2024

Content, progress and pedagogy of the module

In this module students will gain knowledge of how predictive modelling and other data science techniques can be used within organisations or to develop new businesses. This includes identifying challenges when developing data-driven projects – resources, human resources (staffing), collaboration, innovation and innovation capabilities etc. Throughout the module students will explore how data-driven elements integrate with other parts of the organisation or ecosystem as well as the role of leadership of data-driven projects which requires understanding of technical and non-technical elements.

Students will work with a case (existing organisation or new business idea) and develop a business plan or project proposal covering major stages (PoC, MVP, Pilot etc.)

Based on the content above, the students will under supervision write an empirical project with an opportunity to apply a set of data science methods – a combination of techniques covered in previous courses as well as other relevant analytical approaches – to a well defined business problem. This can be in collaboration with an external organization.

After completion of the module, students are able to define an appropriate problem formulation within business data science, identify a sophisticated data collection and analysis strategy, carry out the analysis and present their results using state-of-the-art data science approaches, as well as critically self-evaluate their findings. They can select the most suitable among the wide range of methods presented in the previous modules and autonomously apply it to the specific problem under study.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

  • the representation of business processes and products in terms of predictive modelling or other data-driven processes as well as common limitations.
  • how to define relevant real-world empirical problems within data driven business development including knowledge on the overall architecture and different technical elements of a data-driven project or business model.
  • planning and implementation of data-driven projects in organisations, including issues within technical resources, data foundation, human resources and collaboration with external partners (customers, suppliers, knowledge organisations).

Skills

The objective is that the student after the module possesses the necessary skills in:

  • developing and carrying out a business data science project including data collection, preprocessing, modeling, and performance assessment.
  • mapping, analysing and problem-solving within data-driven projects.
  • using appropriate tools (project management, business planning) for documentation.

Competences

The objective is that the student after the module possesses the necessary competences in:

  • the development of data-driven projects or business opportunities from initiation to Proof of Concept and MVP stages.
  • identifying challenges and developing solutions in the areas of data handling, required technical and human resources and others.
  • communicating of relevant contents (e.g., business plan, project proposal) to technical, managerial, funding and general audiences.

Type of instruction

For information see § 17.

Exam

Exams

Name of examData-Driven Business Modelling and Strategy
Type of exam
Oral exam based on a project
Group examination with max. 4 students. The student may also choose to write the project alone.
ECTS15
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleData-drevet forretningsmodellering og strategi
Module codeKADAT20221
Module typeProject
Duration1 semester
SemesterAutumn
ECTS15
Language of instructionEnglish
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Economics and Business Administration
DepartmentAAU Business School
FacultyFaculty of Social Sciences and Humanities