Business AI and Data Management (Masterseminar / Master, WS25/26, Englisch)

Business AI and Data Management offers a systematic, practice-oriented framework for applying generative AI to real-world data challenges in modern organizations. Situated at the intersection of data management, information systems and artificial intelligence, the course guides students through the end-to-end lifecycle of data-driven processes, from prompt design to toolchain integration and scholarly reporting. Over the semester, participants will: 

• Explore key data management tasks, such as Data Anonymization and Pseudonymization, Deriving Data Quality Rules, Metadata Generation and Cataloging, and Unstructured Data Analysis by leveraging generative AI models.
• Learn to craft, test and refine effective prompts on curated datasets to optimize AI outputs for accuracy, consistency and compliance.
• Build a modular AI toolchain that automates ingestion, transformation, generation and validation of business data.
• Produce a scientific paper documenting their project methodology, experimental results, challenges encountered and solutions devised. 

Students may select from predefined task clusters or propose an original project topic, ensuring alignment with personal interests and emerging industry needs. By course end, they will have gained hands-on expertise in deploying generative AI for robust data governance, quality assurance and insight generation and the ability to communicate their findings in a professional research format.

Seminar work and presentation in English

Kick-Off: 13:15 - 14:15, 21.10.2025, Abt-Jerusalem-Str. 7, Data-Driven Enterprise

Intermediate Presentation: 13:15 - 16:15 (nach Bedarf bzw. Anzahl an Gruppen), 16.12.2025, Abt-Jerusalem-Str. 7, Data-Driven Enterprise

Final Presentation: 13:15 - 16:15, 03.02.2026, Abt-Jerusalem-Str. 7, Data-Driven Enterprise

Participation in the kick-off, intermediate presentation and final presentation is mandatory (no exceptions)

Enrollment: Students will be able to enroll for this course via StudIP. Fixed dates for final enrollment will be communicated shortly.

(Optional) weekly office hours for this lecture: TBD

- coming soon - 

Prof. Dr. Frederik Möller

Julia Schweihoff