In recent years, generating insights from process data landed on a new dimension, especially with advances in data analytics and computing technologies. As a result of increasing competition in the manufacturing industry, it has become a widespread application to monitor manufacturing processes in real-time. Furthermore, advanced industrial applications such as digital-twin require high-frequency and accurate process data. In a typical machining process, workpiece and tool temperatures, tool wear, residual stresses, and part distortions are associated with cutting forces and torques. These forces and torques can be measured at the laboratory using sophisticated dynamometers. However, since these dynamometers are quite expensive and require complex instrumentations, their use is impractical for mass production applications. On the other hand, processing the data taken directly from the machines in real-time with various analytical methods and transforming it into information is more practical and feasible. In this research, a case study was conducted in which high-frequency cutting torque data during the drilling of an automotive part (brake disc) were collected using Siemens SINUMERIK Edge technology. The experiments were conducted with different cutting parameters and under different tool conditions. Using signal processing algorithms and data analytics, the experimental data were correlated with the ground-truth data which is collected by the dynamometer. It was found that the experimental data was highly correlated with the ground-truth data from the dynamometer. Thus, the high-frequency and accurate data required for advanced industry 4.0 applications such as digital-shadow and digital twin, which enable real-time data processing and process-optimization was achieved.