MECE Framework: Mutually exclusive, collectively exhaustive, incredibly effective
Organizations around the world have increasingly become cognizant of the fact that a data-driven approach is crucial to their success. Whether it’s the corporates seeking a competitive edge or governments looking for a sharpened focus on their development agenda, the merits of data have been proven time and again. Today, we have a mountain of information available wherever we look. But it’s easy to go down blind alleys and reach dead ends if we don’t have an adequate framework to help us put all these pieces of information in place and begin our analysis with a coherent plan.
A MECE framework is an excellent tool that can help you do just that. MECE (pronounced me-see) is an acronym for ‘Mutually Exclusive, Collectively Exhaustive’. The concept is just as simple as it sounds. The idea behind MECE is that whether you’re categorizing information, processing data, formulating problem statements or proposing solutions, every point on the list must be mutually exclusive — there should be no overlaps and every item must be independent of each other. Also, all the items on the list together must be collectively exhaustive and express the information in its entirety — no possibility must be missed, the list must account for all conceivable scenarios.
MECE, then, is essentially a thought-process that keeps ideas from getting muddled up. What makes a MECE framework so effective is that once the ideas have been categorized in this manner, they lend themselves to thorough, in-depth analysis very readily. You can go ahead and probe every line of thought that you have listed down to its logical conclusion without fearing the implications that it may have on the other items on the list. Clear segregation at the top goes a long way. Breaking a larger problem down into smaller, distinct chunks will help you attack the issue boldly and reach a solution much quicker than you might otherwise have. Adopting a MECE framework while conducting a survey to collect data, or even while communicating the results of an analysis make the ideas easier to comprehend and absorb.
Let’s have a look at some case studies that showcase the effectiveness of MECE in different contexts –
Categorizing Data
The problem of categorizing school kids into four houses when they join school give us a great platform to illustrate what’s MECE and what isn’t. A grouping mechanism that categorizes students based on their skills — singers, dancers, actors and athletes is clearly not mutually exclusive. There could be many bright kids who are proficient at several of these skills. Besides, the list isn’t collectively exhaustive either. Where are the poets and the painters? The list isn’t MECE and this scheme of categorization has failed us. Grouping the students based on their region of birth (North, South, East, and West) or their zodiac signs, while not the most ideal way to do things is MECE — every category is mutually exclusive and their contents collectively exhaustive. It looks like we’ll have to make do with random allocation of colours to designate the groups (also MECE) until we can find a sorting hat to help us out.
Though the process seems very simplistic, organizing your data in a MECE framework will help you drill down your analysis with maximum clarity and thoroughness. Staying MECE is equally important while presenting your analysis, as we see next.
Communicating Solutions
One of the top restaurants in Dubai has contacted you, a famous chef, to come into their kitchens and help them reinvent their menu that has recently fallen off the pace in the city’s demanding culinary circuit. The management has requested that you direct your focus towards the cuisines on offer, food presentation and the raw material supply chain. After several intense sessions, you come up with a list of key recommendations that can breathe new life into the restaurant –
· Increase focus on Rajasthani cuisine (hugely popular among the city’s glitterati)
· Hire a professional to overhaul the way food is presented to customers
· Identify a new vegetable and fruit supplier
This list is MECE — mutually exclusive since none of the points have an overlap and collectively exhaustive since you have satisfactorily managed to advise the restaurant on every issue at hand. Throwing in another recommendation into the mix such as ‘find a new chef that specializes in making Rajasthani delicacies’ would mean that the list is not MECE anymore; this recommendation overlaps with the first item on the list. If you wish to increase the granularity of your solution, do so using sub-points. This will help you maintain a MECE framework, as seen here –
· Increase focus on Rajasthani cuisine (hugely popular among the city’s elite)
- Find a chef that specializes in Rajasthani cuisine
- Introduce Rajasthani elements to the interior decor
· Hire a professional to overhaul the way food is presented to customers
- Conduct surprise spot checks in the kitchens and in the dining area
- Train every member of the staff on relevant presentation techniques
· Identify a new vegetable and fruit supplier
- Maintain logs to monitor the time of dispatch and delivery
Adopting a MECE framework is equally crucial while listing down the problems that need to be targeted at the beginning of a study, giving it a much needed direction and precision of scope.
Collecting Information
While designing your questionnaire, it is important to ensure that all the questions and sections are mutually exclusive and collectively exhaustive. To make a survey ‘mutually exclusive’, no two questions should be repeated. To be ‘collectively exhaustive’, questions should be chosen in a way that together, they capture all the required information.
It is important to note that the MECE approach applies to both questions and answer choices. Consider the following two questions, and see whether they are MECE or not.
Q1: What is the educational status of all the members of the household?
Q2: Name the highest educated member here.
These are not MECE because we can capture the information needed in Q2 in Q1 itself. We don’t need another question. These questions are not mutually exclusive.
Another example that has been seen during field research very often is a question that asks the respondents both the colour of their ration card and their poverty status. This approach is not MECE since the colour of the ration automatically signifies poverty status. Similarly, in a survey with multiple sections, asking for someone’s occupation in the first ‘Basic Info’ section, then asking if someone is a farmer as the first question of the ‘Farming’ section is not MECE since the data points that these questions seek are not mutually exclusive.
Now consider the answer choices in the following question –
Q1: What is your religion?
A. Hindu
B. Muslim
C. Christian
D. Sikh
Q2: Which category do you belong to?
A. General
B. OBC
C. SC
D. ST
Here, the options of Q2 are MECE — the entities on the list are mutually exclusive and they cover all possibilities. The options of Q1 are not MECE since they do not consider people who belong to other religions — Jains, Zoroastrians etc. The list is not collectively exhaustive. An easy way to ensure that a multiple choice question is collectively exhaustive is to add the option “Other”. If the enumerator chooses “Other”, you can ask the question “If other, please specify”.
MECE can be a way of life and help you organize your thoughts effectively in everything you do. I have known people to create their grocery lists and leave voicemail messages using the MECE framework. Whether or not you decide to take this approach that far, MECE is a very powerful tool that has the promise to make our work a little more efficient every single day.
