Data as a Strategic Asset

Overview

1. Understanding how data can be turned into business insight
2. Utilizing text mining, large language models, and prompt engineering to create business solutions
3. Building and orchestrating agentic AI workflows and AI agents for business processes
4. Equipping a toolset to identify and evaluate infrastructure options for an analytics platform
5. Familiarizing data privacy, security, and compliance, including the regulation of AI
6. Knowing principles and tactics of data governance and data quality management

Description

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, for example by using text mining, large language models, and prompt engineering to create business solutions based on textual data. Building on this, we show how generative AI is moving from single prompts to agentic AI, where AI agents plan, use tools, and execute multi-step workflows on behalf of an organization, and what this shift means for how work is designed and supervised. 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 and AI governance and managing data quality, including associated legal requirements such as information privacy, the GDPR, and the emerging regulation of AI. Finally, we provide an introduction to the infrastructure needed to set up analytical infrastructures on-premise or in the cloud. Throughout the module, inputs from practitioners at leading technology and insurance firms and hands-on group case work connect these concepts to real implementation experience.

After the module, participants…

• have gained practical experience in data engineering to understand how data can be turned into business insight (using Python and Pandas).
• can utilize different text mining techniques and large language models (LLMs) to prepare textual raw data for business decision-making and the creation of business solutions.
• can apply state-of-the-art generative AI technologies, respecting best practices in prompt engineering and agentic AI setups.
• have an overview of the big data technology landscape and are equipped with a toolset to identify and evaluate different infrastructure options for building an analytics platform.
• are familiar with central questions regarding data privacy, security, and compliance, as well as the regulation of AI.
• know fundamental principles and tactics of data governance and data quality management.