From data we build insights, from insights we form knowledge, and with knowledge we can make powerful decisions.

Data Science is Not Magic. Nor is it Science.

Throughout my professional experience, I’ve dedicated myself in one way or another to manipulating information. My work has always involved taking sources of information to transform them, wrestle with them, frame them to make sense, and present them to make them more understandable. To turn them from databases into plain English, making that information speak as clearly to the engineer as to the manager or the salesperson.

On many occasions, I find that this role and that labor in general are rather poorly understood, and sometimes even ignored. In the daily rush of any company, core processes tend to take on excessive relevance due to their immediate nature, and those less urgent take a step back in the relevance hierarchy. There were many occasions where my colleagues, bosses (or myself!) ignored the data in front of us and made decisions with our heart, gut, or even head if we were lucky, but without the data - and without data, no organ can hit the mark.

Let’s demystify the magic behind data science

For that same reason, we also fall into leaving all technological improvement for the (not so) near future. I still remember the face they made when I suggested switching from old Excel to Python to do my work in one of my first jobs. Who has time for that? Didn’t we have to do the presentation for the Marketing team? What the hell is Python? And so, many organizations let the days pass by, leaving modernization for tomorrow. Surely we’ve all participated in this particular procrastination: even knowing that the world moves so fast, we leave our eagerness to catch up for later. Right now there are other things to attend to.

The problem is that nowadays we can’t afford the luxury of either ignoring data or avoiding technological development. Data is our eyes for decision-making, and technology our hands to carry those decisions out. The fact that this reality is so simple to explain but so easy to forget is what has led me to the reason for this article series: “Data Science” is poorly explained, not misunderstood. In other words, the fact that this information-alchemist’s work is ignored is NOT the fault of organizations in their daily rush. The culprit is that magician and their entire guild, who in their eagerness to improve their spells and potions tend to forget that the most relevant part of their work isn’t doing magic, but explaining the trick.

Therefore, I’ve decided to relate, in language for people, how exactly this task of hunting information, cooking information, serving information, and eating it al dente is performed. And also, explain why this process isn’t exclusive to “Data Scientists,” nor engineers, much less statisticians or other sorcerers. Finally, to tell the best-kept secret of that coven I’m part of: This magic is less science, and more art.