Laser Metal Deposition (LMD) is an Additive Manufacturing (AM) method for producing near-net-shape parts. Maintaining consistent part quality over several production cycles remains challenging due to accumulating process instabilities. This work focuses on the integration of sensor systems and the development of a data-driven digital shadow to monitor and analyze long-term process behavior under constant process conditions. A comprehensive sensing architecture was implemented, including process sensors like Optical Coherence Tomography (OCT) and melt pool camera, environmental sensors, and an edge computing system for synchronized data acquisition and data pre-processing. Defined specimens for mechanical testing were produced over a series of repeated printjobs using Ti-6Al-4V and fixed process parameters. All process and machine data were collected to capture the state of the process and potential degradation effects. The primary goal was to evaluate sensor integration and establish a structured digital shadow environment capable of supporting future in-situ process anomalies. Data analysis focused on signal variability, detection of instabilities, and exploration of potential links between sensor outputs and part quality indicators. Observations point toward the feasibility of using sensor data to track subtle changes in the process, forming the basis for predictive models and automated in-situ quality assurance strategies, which will reduce the necessity of post-process quality assurance. This approach demonstrates a scalable framework for enhancing the reliability of LMD process through consistent monitoring and early-stage anomaly detection.