How to improve your analysis?
During last year I was preparing engineers and business students around the world to enter in the top Strategy Consulting Firms like: McKinsey , BCG and Bain . My focus was case interview, which is the core of the application process of these companies.
In this article I am going to explain 3 aspects: what is a case interview, a common mistake during the case interview and how to solve it with the MECE Principle.
First aspect, case interview consists in a project’s simulation with a consultant, who explains you a problem and you have to solve it. For that you create a route-map to analyze the entire problem, before giving the solution.
As you know, consultants are paid for results. So they don’t normally have too much time to analyze all the possible data, or repeat the analysis a couple of times.
So they have to prioritize the most important aspects to analyze and be careful decomposing the problem, and in consequence the analysis.
Second aspect, the typical problem; when candidates do this analysis, many times they divide the data: leaving parts of it or treating it more than 1 time. So they don’t do a good analysis because on one hand, they are not considering the data correctly; and on the other hand, they are not being efficient because they are treating the data too much time.
So, the third aspect, of how to solve this problem. Consultants frequently use the MECE Principle. This principle means “Mutually Exclusive and Collectively Exhaustive” . It is a grouping principle for separating a set of items into subsets, the choice of subsets should be:
- Mutually exclusive: no subsets should represent any other subsets (“no overlaps")
- Collectively exhaustive: the set of all subsets, taken together, should fully encompass the larger set of all items ("no gaps").
The MECE principle is useful to map process or create structures where the optimum arrangement of information is exhaustive and does not double count at any level of the hierarchy .
Examples of MECE arrangements include: categorizing people by age (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).
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