Granular Computing can be viewed as an umbrella term for these theories, methodologies, algorithms, techniques, and tools in information processing that make use of information granules in the process of problem solving. It attempts to organize and unify several, sometimes seemingly distant approaches that so far have been developed in isolation. By examining all of these existing studies in light of the unified framework of granular computing and extracting their commonalities, it may be possible to develop a general theory for problem solving.

In general sense sense, granular computing is meant to describe a way of thinking that relies on the ability to perceive the real world under various levels of granularity (i.e., abstraction). Such granular way of perceiving the real world is characteristic for humans, who in order to abstract and consider only those things that serve a specific interest switch among different levels of granularity (preciseness, exactness).

Several well known approaches can be interpreted and integrated using granular computing principles. Fuzzy and rough methodologies fall into granular category quite naturally, but other paradigms in computer science and engineering such as artificial neural networks, Bayesian reasoning, clustering or sensor fusion can also be represented as granular. Fitting various approaches into common granular framework makes it easier to construct compound systems that solve real life problems.