Today’s production is characterized by shorter innovation cycles, leading to a highly dynamic shop floor environment. To tackle the need for fast and flexible adaptation, it is aimed for a complete digital integration of machines and a comprehensive data management. Benefiting from the vast amounts of heterogeneous data collected requires a thorough understanding of their dependencies and correlations as well as a human-assisted, yet automated analysis process. Still, up to this date, the realization of this vision proves to be very challenging in practice and requires a myriad of different new methods and technologies. Therefore, the scope of this paper is to provide an outline of how such an end-to-end connectivity can be achieved along the real-world example of a lightweight construction process. As such, it is shown how its machines are upgraded and connected to the network in order to allow for collection of time series data using a central agent. A context provider is introduced, with which external data sources, such as wearables, can be integrated. After collecting and merging the data, its storage and sharing are managed using a Data Lakehouse. The data is analyzed by using a human-assisted feedback loop, effectively integrating expert knowledge into the learning process. Using the so-gained knowledge, optimization of the shop floor, intralogistics and IT infrastructure can be achieved. Preliminary results from parts of this loop already in operation suggest significant potential benefits, paving the way for more comprehensive integration across production systems.