Dataspace Harmonisation
Harmonisation across data spaces enables effective data sharing and collaboration, and it is essential to overcome the inherent challenges of data ecosystems which currently limits the economic value of data. The EU Data Governance Act specifically aims to enable cross-sectoral interoperable frameworks of common standards and practices to share or jointly process data for development of new products and services, scientific research or civil society initiatives in order to increase the European digital economy to > €800B by 2027.
Digital Product Passports (DPPs), as part of the EU Eco-design for Sustainable Products Regulation (ESPR), are an example use-case for cross-sector data space harmonisation, which can be used for supporting services that rely on data throughout product lifecycle stages, such as total cost and carbon footprint reductions.
The DIP team at BRIL is actively developing technologies that support trusted cross-sector dataspace harmonisation, such as cross-domain identity mapping and verification using multiple attribute assurance services and digital twins, knowledge extraction and sharing as well as cross-sector compliance evaluation using Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) methods. The aim is to support interoperation across both European and International dataspaces to facilitate global data sharing ecosystems to streamline global operations, by avoiding duplication of systems and processes, including within customs clearance and other sector specific traceability.
An example use-case for this is in battery product optimisation throughout first use applications as well as repurposing for second and subsequent uses and finally remanufacturing and recycling stages. Once the batteries are manufactured from raw materials, they are embedded in vehicles within the automotive sector and can be subsequently used for energy grid storage in different countries before recycling back to raw materials and then into new batteries. Therefore, lifecycle optimisation requires the collection of the data from all stages in multiple sectors and geographical regions.
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