In organizational management, it is highly recommended that data and information be adequately prepared to match the knowledge needed to be used in decision‐making processes. However, faced with the paradigm of complexity that currently dictates the dynamics of modern organizations, there is still a search for operational solutions that allow agility and flexibility to corporate information flows to meet that desired condition. In this context, the concept of data and information governance presents itself as a fundamental premise because it systematizes, reorganizes and reorients each element of the organizational system (people, processes, structures, etc.) without losing the notion of its contexts and causalities. For this, in the conceptual modelling of governance, the concept of systemism arises to support the balance between holistic and reductionist approaches, inherent in management processes, but often considered antagonistic or contradictory. The present chapter presents and discusses a data and information governance model for research and development (R&D) organizations. The model is based upon the concepts of data, information and knowledge life cycles and knowledge mapping, recovering and valuing the ontological nature of the elements of the system under analysis and constructing a pragmatic proposal for corporate application and operation.
Part of the book: Ontology in Information Science