Abstract
In the plethora of conceptual and algorithmic developments supporting system modeling, we encounter growing challenges associated with the complexity of systems, diversity of available data and a variety of requests imposed on the quality of the models. The accuracy of models is important. At the same time, the interpretability and explainability of models are equally important and of high practical relevance. We advocate that the level of abstraction at which models are constructed (and which could be flexibly adjusted), is conveniently realized through Granular Computing. Granular Computing is concerned with the development and processing information granules - formal entities that facilitate a way of organizing and representing knowledge about the available data and relationships existing there. This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in the realization of models.