We are used to jumping to conclusions really fast, without analyzing all sides. As such, when trying to understand the world, intuition frequently fails. Here I propose a different system for doing Data Science without "trusting your gut".

Disclaimer: I’m not talking here as an expert in Common Sense or intuition. I’m only stating that it does not work all the time with Data Science.
Our Common Sense

We have common sense: a way of seeing and understanding things based on what has been "good" for us as a species. That could be a definition that we could expand into something more technical:
Sound practical judgment concerning everyday matters, or a basic ability to perceive, understand, and judge that is shared by ("common to") nearly all people.
According to this definition, if we want to follow common sense, we need to understand and judge based on what is shared with "most people".
Ok, don’t get me wrong, this is important in some aspects of life. I mean, if a dangerous animal is approaching you, it is common sense to run or just generally do something to keep yourself alive. It is also common sense not to eat the plant that has killed half of the village. That’s more than fine.
But as I stated before, the common sense that reigns in our culture is sadly Aristotelian and Medieval (Études d’histoire de la pensée scientifique – Alexander Koyré). This means that intuition fails a lot of times when trying to understand the world (imagine scientists still thinking that rocks fall to the ground because that’s their natural place!). "Common sense" sometimes comes with poor judgement, creating a bias in the way we see things.
We are used to only seeing what is right in front of us and "trusting our gut".
Our Intuition
I mean, I tried to write something here, but I’ll just let three definitions and three pictures do the talking.
The ability to acquire knowledge without proof, evidence, or conscious reasoning, or without understanding how the knowledge was acquired.
Wow.
A thing that one knows or considers likely from instinctive feeling rather than conscious reasoning.
Oh my…
The ability to understand something immediately, without the need for conscious reasoning.
Ok…
And the great images by Scott Adams:

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Common Sense and Intuition in the Data World
As expected, obviously, we translated our common sense and way of "doing things" into our companies and our work life. Although it is true that both common sense and intuition made millionaires a while ago, now the world has changed. It is now very unlikely that people that trust their uninformed instincts can beat others that take their decisions from a deep study and analysis of the world and the Data we have.
Something interesting to think about at this moment is that knowledge is not inside us – it is in the space between us. It is just there, waiting for us to interpret what it has to say.
Ok, I know this sounds kind of weird, but before judging (or trusting your gut who’s screaming, "Nonsense!"), first read more about it:
So what does Data Science have to do with any of this? Going beyond common sense and intuition is the only way of solving complex business problems. In a world full of intuitive models, disruption and advancement come from going a bit further, using data to understand what cannot be seen with the naked eye or with an "expert look".
As Russell Jurney stated in the "Manifesto for Agile Data Science"
In software application development, there are three perspectives to consider: those of the customers, the developers, and the business. In analytics application development, there is another perspective: that of the data. Without understanding what the data "has to say" about any feature, the product owner can’t do a good job. The data’s opinion must always be included in product discussions, which means that they must be grounded in visualization through exploratory data analysis in the internal application that becomes the focus of our efforts.
Again: The data’s opinion must always be included in product discussions.
Repeat that in your head. It is very important.
We need to listen to what the data has to say. Stop trusting that we know better than data all the time. If our "experts" in the area can solve everything, what is the need for a Data Scientist? Models either come from data (not anecdotal "experience" or Intuition), or aren’t models at all.
I think the first step towards making a data-driven organization is proving to the team, directors, managers and board that "listening" to the data, using it and understanding it is way better than using our feelings. In other words, they need to know that this will work, and that informed decisions are made only after a whole analysis and Data Science Cycle is concluded.
I would love to hear what you have to say about this, and share your thoughts on the subject.
Thanks for reading this. I hope you found something interesting here 🙂
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