About this course

Learn why data management matters for industrial AI and HPC (Section 1), what AI-ready data looks like in practice (Section 2), and how FAIR-aligned (Findable, Accessible, Interoperable, Reusable) lifecycle practices turn data into a scalable, trustworthy asset (Section 3).

📚 How to read this course

🌱 Who this course is for (and who it is not)

This course is designed for industry professionals working with AI, data, or high-performance computing, including data engineers, AI/ML practitioners, software and systems engineers, architects, R&D teams, and technical project leads. It is especially relevant for organizations developing AI solutions in HPC and AI environments, where data quality, structure, and lifecycle management directly affect cost, performance, and reliability.

The course assumes basic familiarity with data and AI concepts but does not require deep prior knowledge of data management or formal FAIR theory. Its focus is practical and operational: how to manage data so that AI and HPC workflows work effectively in real industrial settings.

Instead, it provides a shared understanding of good data management practices that enable scalable, trustworthy AI in industry and points learners to more specialized materials when needed.

🎓 Learning Objectives (LOs)

After completing this course, participants will be able to:

1. Understand why data management is critical for industrial AI and HPC, including its impact on cost, performance, scalability, and regulatory compliance.
2. Recognize what AI-ready data means in practice and identify the data quality, structure, and governance requirements needed for successful model training and automation.
3. Apply FAIR principles in a business context, enabling data that is findable, accessible, interoperable, and reusable without making it public.
4. Design and manage data across its full industrial lifecycle, from planning and collection to processing, preservation, and reuse, with AI and HPC workflows in mind.
5. Use key tools and enablers, such as data catalogs, metadata standards, persistent identifiers, and knowledge graphs, to improve discoverability, automation, and collaboration.
6. Connect good data management practices to real business value, including faster AI development, better use of HPC resources, reduced risk, and cross-team reuse of high-value data assets.