From Data to Insights: Driving Corporate Performance (Summer Term 2023)

Field(s) of Study
6 CP/ECTS, equivalent to 4 SWS
(if your module is not listed, please refer to your module compendium and your degree's course manager to ensure compatibility with your track of study)
Summer, Firm Term
Teaching Format
Lectures, Tutorials, Case Studies
Written Report (in Groups), Presentation
Course Language
Learnweb Platform
Page last updated
March 13, 2023



For attending this course, you will need to apply via e-mail (
The deadline for applications is March 27th, 2023 at 12:00 pm (noon).

Your application package should consist of your CV, your transcript of records, and a signed letter of commitment, which you can find here.
Please ensure that you have carefully read the letter of commitment, as your adherence to it is vital.

We will inform applicants and confirm participation shortly after the application deadline via e-mail.
If there are still any vacancies after the deadline has passed, applying until the end of the first week of the course may be possible.


Course Description

The role of data in corporate decision making has gained much importance and the ability to collect, clean, merge, investigate, and interpret data have become important skills. For every manager and also for the majority of business employees it is important to understand the power and limits of data analysis to make better decisions. With regard to management accounting, the task at hand is to use structured and unstructured data in organizations to detect critical performance drivers and measures, and to identify causal relationships in firms to improve managerial decision-making and management control. Hence, this course covers data-driven performance measurement in modern firms with an emphasis on applied empirical methods.
The module is based on case studies to show how data can be used to find solutions for different management problems. All students are expected to conduct (guided) real-time programming in class using Stata as the primary software for which no prior knowledge is required. With regard to methodology, this course also prepares students for writing an empirical seminar or master thesis and for doctoral studies in the area of business in general.

For further information, please refer to the current module compendium.


Course Schedule

Tuesday 9:00 am -
2:00 pm
Lecture Weekly 04.04.2023 - 16.05.2023 ST A 1

12:00 pm -
4:00 pm

Exercise (except 06.04.)

Weekly 06.04.2023 - 11.05.2023 ST A 1


Additional Information

More information on the course can be found in this course description and informational and non-binding document on grading.



Prof. Dr. Martin Artz (responsible)

Silvan Kindinger (accompanying)