Married at First Sight
Dreams, Data , Disaster22 Nov 2016
I’m ashamedly a fan of terrible television so, i’ll admit I’ve been watching a show called “Married at first sight” recently in which two people are matched “Scientifically” based on various social,psychological and biological parameters, they meet and marry each other immediately, without so much as a conversation, hence the name I guess. The premise of the show is to test if you can find your soulmate scientifically.
There were four couples in total but despite being “perfect matches” based on the data that the scientists gathered, only one out of the four couples chose to stay together in the end.
Even the handsome Clark and his glamorous beau Melissa, who looked as if they were a scientific match made in heaven look like they are on the rocks after Clark made a fateful move to Milton Keynes from London to be with his new wife of three weeks. I started to wonder why this experiment had such a high failure rate when scientifically speaking, all four couples were a perfect match (taken with a pinch of salt of course).
This lead me to KDnuggets where I found article after article talking about the pitfalls of undertaking a data project e.g. “3 Reasons why your Data Project will fail”, “10 pitfalls to avoid when “how to Overcome the challenges of a Data Project” etc, after reading this I realized the weak link seems to be, more often than not, people.
This is true not just in Data Projects but within many other highly automated processes, the vast majority of automotive and aviation accidents are caused by operator or user error error (around 80%) – I’ll write more about this later in the week.
People are emotional, highly irrational and complex which sometimes leads to a complete oversight or sometimes outright rejection of clear, rational unemotional data.
I’m not a data Scientist but I’m fascinated by what you guys do, so just out of pure curiosity. How do you minimize the dreaded human element when considering your internal stakeholders or when you are actually building the framework of a Data project?
Any comments and thoughts thoughts friendly or otherwise are welcomed!
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