The best AI tools are useless if they cannot access the right data or if the required data has not been semantically processed and cannot be exchanged interoperably. Andreas Faath, Managing Director of the VDMA Machine Information Interoperability (MII) department, explains in an interview what role data spaces play here and why they are so important for the value-creating use of AI.
Mr. Faath, why should the manufacturing industry start looking at data spaces now?
The manufacturing industry should already be taking a close look at data spaces, as they play a central role in the digital transformation and Industry 4.0. They are an integral part of the EU data strategy for various strategic areas, including the manufacturing industry. Data Spaces enable the secure and sovereign exchange of data between different actors, leading to improved collaboration, more efficient processes and the development of new business models. By using data spaces, companies can optimally prepare themselves for future requirements. The VDMA is involved in the SM4RTE-NANCE, RoX, Factory-X, Wind-X projects and the Manufacturing-X initiative to strengthen the competitiveness and resilience of German and European industry through digitalization.
What minimum technological requirements should be met in order to benefit from data spaces?
Companies should have a good digital infrastructure in order to make the best use of data spaces. This includes high-performance networks that guarantee fast and reliable internet connections. Data management systems that can manage and store large volumes of data are also recommended in most cases. Finally, interoperability is important so that data can be exchanged between different systems using standardized interfaces and protocols. However, the biggest hurdle for many companies is not of a technical nature, but rather the heterogeneity of data models and quality. This data can only be used to build scalable business models if there is transparency about the company's own data. This can be achieved through semantic modeling.
A key factor for the use of data spaces is trust in them. How can trustworthiness be increased?
The trustworthiness of data spaces can be significantly increased through a variety of measures and strategies. Policies are central to the concept of data spaces. These are rules that each actor in the data space sets for their data and that define who can access the data and under what conditions. Once these policies have been defined, they can be read and interpreted automatically. This enables the completely autonomous processing of business cases between several players in the data space.
What potential can artificial intelligence unlock in the context of data spaces?
Data spaces are crucial for the use of AI. They enable the provision and use of large amounts of data from different sources and different companies, which significantly increases the amount of data available. This creates rich data availability for AI systems to perform more accurate, scalable and comprehensive analysis. However, the use of data spaces also brings challenges. The large amount of data and the variety of sources make it particularly challenging to process the data correctly and efficiently. It is therefore advisable to use standardized data models wherever possible in order to reduce the heterogeneity between data sets. It also requires smart AI algorithms and good data management strategies to ensure that the data quality remains high and the analyses are reliable. To answer the question in conclusion: In the context of data spaces, AI can unlock numerous potentials. It can efficiently analyze large volumes of data and identify patterns within them. AI significantly increases efficiency by automating and optimizing processes. AI models can make precise predictions and support companies in their decision-making. In addition, AI enables the provision of personalized solutions and services based on the analyzed data.
