Considering the advent of Industry 4.0 and development of Cyber-Physical Systems, a large amount of data has been collected in production systems. Available business process recommendations, presented in Business Process Management Notation and Case Management Model Notation frameworks, have been enough to represent the systemic and relational characteristics of production systems and their components. Recommendations for data life-cycle management do not consider explicitly combining the elements of the modeled process and the representative data of the production system assets. In fact, process flows can represent data generated or consumed but do not have an explicit associated action to their data: generation, transformation and consumption. From a Performance Analytics perspective, approaching data and processes in an associated way can be an advantageous practice in management and decision-making. This study uses one investigative experience in the automotive industry, from two aspects: the As-Is mapping of a segment of the measurement system after the welding step, and the design and implementation of Big Data Analytics architecture related to the same process. The result is a proposal of an associative framework between processes and related data, which are following the recommendations of currently applied frameworks for Business Process Management and Big Data Analytics.