Multi-granularity data is a special useful type of data which is able to show data in different granularity spaces by using different granularity forms of a universe of discourse （i.e. a set of research objects）， and then multi-level knowledge discovery can be studied based on multi-granularity data. As is well-known， quotient space theory， sequential three-way decision， multi-granulation rough set， multi-scale data analysis model and multi-granularity formal concept analysis are several common and effective multi-granularity data analysis methods， and they have attracted more and more people’s attention. This paper reviews the existing work on multi-granularity data analysis in granular computing， gives theoretical frameworks， basic notions and main research ideas for each kind of multi-granularity data analysis methods， and points out some problems for the further study of multi-granularity data analysis. The obtained results can provide a theoretical reference for future research of this field.