The number one problem-solving tool used by the biggest consulting companies such as McKinsey, BCG, and Bain, for both structural analysis and efficient communication. Based upon making MECE divisions and segmenting data at every step of analysis while solving a problem, this method requires a sharp mind and lots of attention.
With it being the fundamental rule for structuring your whole analysis, making a single mistake at any level of the subdivision will result in the framework being not entirely MECE, hence possibly derailing your entire case.
Defining the MECE principle
A way of organizing information based on one principle – the created subsets being mutually exclusive and collectively exhaustive (MECE). Simply put, the mutually exclusive part means that each subset is independent of any other.
The collectively exhaustive, or completely exhaustive as some would call it, means that all of the relevant entities are considered, and not a single relevant entity is left that can’t be assigned to one of the subsets. Basically, all possible options have been considered.
The MECE principle is usually displayed in the form of an issue tree (logic tree), as it helps to better isolate sub-problems and give a better overview of the whole structure while you branch your way down to the core of the problem.
MECE vs non-MECE Example
To understand the difference between MECE and non-MECE subdivisions, we’re going to set an example. Let’s divide the world’s residential buildings into two subsets: Those with at least 20 stories and those that have less than 20.
This is a MECE case – the two subsets are mutually exclusive, as a building that has let’s say 34 stories, clearly falls into the 20+ category, and cannot find itself in the other, just like a 7-story building can’t be sorted into the 20+ subset. At the same time, it is collectively exhaustive, as it covers all the residential buildings, ranging from one to, figuratively speaking, infinite stories tall.
If the subsets in this example were: buildings with at least 20 stories and buildings that have 15-35 stories, this would be a non-MECE case, for obvious reasons. Primarily, there is an overlap in these two subsets. If a building has 17 stories, it falls into both categories, making it not mutually exclusive.
And on the other hand, this only includes buildings ranging from one to 35 stories, so it doesn’t exhaust the relevant field, as any building taller than 35 stories would not fall into any subset, making it not collectively exhaustive.
Why the MECE approach?
Problem-solvers and decision-makers often find themselves using non-MECE problem structuring methods when working on anything client-related or customer-related. This approach is not optimized and will most likely lead to irrelevant data being collected, as well as duplication of labor.
Strategic and reliable
A non-MECE approach may seem faster and the data collected may seem useful at first, but in the long run, it will often lead you on a wrong path and complicate your patterns as it does not have the strategic approach that the MECE principle gives.
The MECE principle will help you keep your focus on the main categories and build your ideas from the top-down, growing branches on your issue tree as far as they need to grow to reach the goal, which is solving the initial problem.
With the MECE approach, you make certain that you exhaust the field of interest and leave no stone unturned, at the same time making sure you subdivide your problem into subsets that do not overlap, hence guaranteeing that you never have to do the same work more than once.
It does however require careful planning and attention to detail and more often than not, beginners will find themselves reworking their patterns due to finding flaws in their subsets as the issue tree starts to branch out more and more. But once you’ve got the hang of it, the MECE principle can be applied to any type of work-related or personal concern.
MECE Principle practical use
Although there is no set-in-stone rule for this, and it varies from case to case, there are some guidelines that can help you get the best out of this principle and get the best results.
The first guideline would be that there can be no overlapping among the sets. An item can appear only in a single category (mutually exclusive), if it appears in more than one – rethink your setup. Sometimes this can be caused by something as simple as a typing error, so always double-check everything.
Guideline number two would be the collectively exhaustive rule, therefore there can be no loose ends and everything is accounted for. No relevant item is left outside the subsets.
Guideline three is to always keep an eye out for logical errors, no matter how perfect the framework feels. There is always space for possible oversights or fallacies so examine your assumptions wisely and look for irregularities.
Guideline four acknowledges that the small elements must parallel one another – meaning that the categories in MECE classification should be directly comparable whenever possible.
Guideline number five is the old writing principle known as “the rule of three”. It implies that sets of three items tend to be more memorable and have the smallest amount of information to create a pattern. While this cannot always be the case and sometimes only 3 categories will not be enough, it is advised that you keep the number below 4 whenever it’s technically possible.
To Wrap it Up
Taking everything into consideration, the Mutually Exclusive Collectively Exhaustive (MECE) principle has many upsides and very few downsides. Overall it will help you reach better conclusions, manage your collected data in an optimized way, and make you a better, systematic problem-solver. Once you master it, it will become a part of your mindset and affect the way you think and make all decisions, personal or professional.
Many giants of the consulting world such as McKinsey, Boston Consulting Group, and Bain & Company rely on this principle and will make sure any newcomers to their business are well acquainted with it. Now I am not saying they are only successful because of the MECE principle, but it casts a pretty good light on it, doesn’t it?
