Today, the efficient collection, storage and management of research data is more relevant than ever. Ideally, the related data infrastructure should be developed based on FAIR principles of findability, accessibility, interoperability, and reusability. To comply with these principles, and to ensure the reproducibility of scientific work, metadata is just as critical as data produced in the experiments. Metadata describes how experiments were performed and in which context they were performed. In the project "Establishing Structures for an Efficient Management of Materials and Processing Data" we aimed at developing workflows and infrastructure for the collection, storage and management of such data in the Laboratory for Surface Science and Coating Technologies at Empa. The goal was to first identify the most important types and classes of metadata, which are relevant for our day-to-day research. For this, we collected data types and their relationships within our own laboratory, but also discussed our findings with colleagues at Empa and neighboring institutes working on similar topics. In a second step, we evaluated different software solutions for the collection and storage. When evaluating our workflows and the data management solutions in question (e.g. openBIS) it became clear, that very involved customization would be required. Several potential tools have been identified which could be implemented in the future including openBIS, NOMAD Oasis, or other custom solutions (https://doi.org/10.1021/acscombsci.0c00057). While the implementation of a suitable metadata management solution is still ongoing, we were able to make some significant progress in related efforts. For instance, it was an important part of the project to streamline the metadata collection as much as possible. This meant modifying our instruments, whenever possible, to the point where metadata collection would happen automatically. In the last year, we were able to implement automated metadata collection in several of our instruments as well as implement streamlined import and processing of this information into our data engineering platform combIGOR. This serves as a valuable starting point and we will continue to modify the software of additional tools in the future. Additionally, the data structures and requirements we defined throughout the project will facilitate the ongoing efforts for a digitalization of our workflows, which might also benefit colleagues in other laboratories.