Static and dynamic process data of a system are accessed. Thereafter, using this accessed process data, a subset of such data forming relevant data for a particular context is derived. The data is then explored using a computer-implemented process or processes to automatically get insight into information about structures, distributions and correlations of the relevant data. Rules can be generated based on the exploring of relevant data that describe data dependencies within the relevant data. These generated rules can later be used to generate synthetic data. Such synthetic data, in turn, can be used to for a variety of purposes including the training of a machine learning model while, at the same time, complying with applicable privacy and data protection laws and regulations.