MECE principle
From Wikipedia, the free encyclopedia
The MECE principle (where "MECE" stands for "mutually exclusive and collectively exhaustive") says that data should be divided in groups which do not overlap and which cover all the data. This is desirable for the purpose of analysis, because it avoids both the problem of double counting and the risk of overlooking information.
The MECE principle is useful in the business mapping process. If information can be arranged exhaustively and without double counting in each level of the hierarchy, the way of arrangement is ideal.
Examples of MECE categorization would include categorizing people by year of birth (assuming all years are known). A non-MECE example would be categorization by nationality, because nationalities are neither mutually exclusive (some people have dual nationality) nor collectively exhaustive (some people have none).
[edit] See also
- Case analysis
- Partition of a set for a mathematical treatment
