Hi my name is Jesper and I make videos about data architecture
and digital transformation for YouTube
this is part two of the pyramid principle where i will explain the messy framework
if you haven't watched my first video click on the link above
in part one i explained the sc qa framework well that's quite a mouthful isn't it
which is really about how to create the hypothesis but once we have a hypothesis we need to do
something with it and that's where this video comes handy this video is about understanding how
we're creating the arguments and logic to support hypothesis using inductive and deductive logic
inductive and deductive logic originates from greece well to be specific from ancient greece
and they were both developed by dude you have probably heard of aristotle's
both inductive and deductive logic both strive toward coming to truth or to a conclusion
but they approach it very differently let's start with deductive logic deductive logic begins with
a broad truth that is then followed by a major premise which is followed by minor premises
so let's use a very famous example a major premise might be socrates is a man
the minor premise would be all men are mortal and then follows the conclusion all men are mortal
this is an example of deductive logic
if the major premise is true and the minor premises are also true then the conclusion
must also be yes true deductive logic is about right and wrong black and white true or false
hence it's very often used in a scientific context to prove that something is right or wrong
inductive logic involves drawing conclusions from premises
the conclusion drawn is not necessarily about right or wrong
it is more about the probability of the strength of the conclusion and it often changes over time
for example if someone is slurring their words and they smell of alcohol they might be drunk
so although the premises are true they are slowering their words and they smell of alcohol
it doesn't necessarily mean they must be drunk there can be other reasons whereas
deductive logic is used in science inductive logic is more used in day-to-day life
and this how we are formulating and evolving our beliefs over time
in business inductive logic often comes first and then we use deductive logic to support it
so it may work something like this we're using the scqa framework to create hypothesis
then we are using inductive logic to identify a number of scenarios supporting that hypothesis
and then we finally use deductive logic to prove the scenarios
mesa is a framework for inductive logic and more specifically an approach
or a framework for creating strong premises to support the conclusion or a hypothesis
now message stands for and this really is a mouthful mutually exclusive and collectively
exhaustive so these two frameworks are ways of thinking they actually work together
so mutually exclusive means that each premise must stand on their own one premise can't depend
on another collectively exhaustive means that all premises together must fully support the argument
or the hypothesis that can't be something missing let's start with mutually exclusive
it means that can be no overlap between premises a venn diagram explains how this works
a good example is the use of h brackets if the h brackets don't overlap they are mutually exclusive
if they do overlap they're mutually inclusive the problem with overlapping premises
is a lack of clarity they bring as they depend on another premise to be complete
collectively exhaustive means that all premises are covered that leads
to the conclusion again using 8 brackets it means that all 8 brackets must be covered
the problem with missing premises is that the conclusion is not fully supported
put together it looks something like this no age bracket overlaps with another and taken together
they represent a full age cycle that is no age bracket is left out for example this would be
a clear and good way of pitching a marketing strategy but if we rearranged it into non-message
structure along these lines well as you can see a marketing strategy would be rather confusing
the hypothesis that we want to support is this
to support our medium and long-term goals and aspirations
then we need to redevelop and build an automated and data-driven crm so the question is this
how can messi help us how can mess help us come up with a premises to support this hypothesis
there are several ways of approaching it and this is how we did it
firstly we identified a number of crm issues and by looking at them it is clear that they are not
messy for example poor analytics and reporting and poor quality data are clearly related
secondly we grouped issues together
remember inductive logic is not absolute and conclusions will depend on the situation
in which they're analyzed so in our case the following grouping made sense
third we tested if groups were mutually exclusive increase in cost of selling is about profit
whereas cost of ownership is about cost profit and cost are mutually exclusive
software support is unrelated to profit and cost and hence also mutually exclusive
regulatory and legal issues could potentially been related to ending software support but in our case
they were related to poor quality data so yes for us the four groups are mutually exclusive
and fourthly we tested if the groups were collectively exhaustive
there were no missing groups or premises making it collectively exhaustive
hence we came to the conclusion that the groups or premises were messy
i hope this has explained inductive and deductive logic and how to use the messy framework to create
strong arguments or premises supporting a hypothesis if you enjoyed this video
please hit the like and subscribe button thank you for watching and see you in my next video