1. Understanding how data can be turned into business insight
2. Utilizing large language models and prompt engineering to create business solutions
3. Getting an overview of the big data technology landscape
4. Equipping a toolset to identify and evaluate infrastructure options for an analytics platform
5. Familiarizing data privacy, security, and compliance
6. Knowing principles and tactics of data governance and data quality management

Data is the fuel that makes big data analytics and artificial intelligence run – this is the central theme of the second module. We teach you how to turn data into business insight, e.g., by using large language models or prompt engineering to create business solutions based on textual data. We cover all organizational, technical and legal requirements for managing data as a strategic asset and enabler for data-driven business transformation. Participants understand that 80% of the effort associated with big data and artificial intelligence projects is data engineering: the process of acquiring, modeling and cleaning data. We introduce central concepts for establishing effective data governance and managing data quality including associated legal requirements such as information privacy and the GDPR. Finally, we provide an introduction to the infrastructure needed to set-up analytical infrastructures on-premise or in the cloud.