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Please note that the training course will be held in German.

Modern data management concepts differ significantly from conventional approaches like spreadsheet-based data storage or traditional databases – both in terms of use and underlying technology. Often, data is scattered across silos, stored in inconsistent formats, or even lost during the course of a project. The result: valuable knowledge remains untapped, traceability requires extensive manual effort, and data-driven optimization or AI applications fail due to poor data quality.

Focusing on material and process data, this training will teach you how to make such data findable, linkable, and reusable – using FAIR data principles, data spaces, ontologies, and knowledge graphs – with practical examples from material, experimental, and process data. This approach increases transparency and accelerates analytics and decision-making processes.

Data Management for Material, Experimental, and Process Data

Materials processing is a highly complex procedure (from material selection through component manufacturing and usage to circular economy) that generates diverse, often heterogeneous data – much of which is later lost. Keeping an overview of all data, managing information flow across process and work steps, and storing or cataloguing data in a sustainable, integrated way is correspondingly challenging.

Modern data management concepts address these issues by connecting data to create transparent and traceable workflows. By integrating and linking data, knowledge about materials and production processes becomes digitally accessible. This allows new knowledge to be generated and used – for example, to improve product quality, productivity, or resilience, and even to develop new business models.

Our Expertise, Your Benefit

In this training course, you will get to know the fundamentals of modern data management concepts with a focus on material, experimental, and process data. This includes, among other things, semantic technologies (especially ontologies), knowledge graphs, data spaces, FAIR (Findable, Accessible, Interoperable, Reproducible) data storage, and the description of data processing workflows. At the end, the participants will understand how and under which conditions the resource material, experimental, and process data can be efficiently accessed and utilized in processing operations and in component applications through modern data management concepts.

After the training, you will be able to:

• Identify and explain key concepts such as ontologies, knowledge graphs, data spaces, and FAIR data
• Communicate the fundamentals and benefits of modern data management approaches within your company
• Apply semantic technologies to simple examples and translate them into practice
• Evaluate which implementation will be most beneficial in your work environment

Target audience

The program is designed for participants from industry and academia (e.g., researchers, laboratory staff/technicians, and employees in research and development who work with the storage and analysis of materials and process data) who wish to either gain an introduction to the fundamentals of modern data management concepts or update their existing knowledge in their company or area of work. No prior technical knowledge in the field of data management is required.