Invariably, the mystery deepens as puzzlers wrestle with the idea of a data and science mash-up. And as conversations continue, the whole thing sounds more and more like witchcraft.
I work with car manufacturers like Volvo, to design a future where no passenger is ever hurt or killed in an accident again. Think what that would mean. Imagine the impact on all of us.
At the same time, I’m working with Amazon-type companies to ensure that your purchase is moved to the nearest distribution centre before you’ve even clicked the buy button. Then delivered in 60 minutes.
Just a couple of examples that spark interest and curiosity among friends. But can a data scientist magic such amazing results from data and analytics… and fresh air?
Well, on its own, data is simply numbers and words; digits and characters. Similarly, analytical algorithms are mathematical formulae translated into code. And applying a random algorithm to an anonymous dataset is like fishing in the dark, and the chance of success is about the same.
The fact is, a dataset has to be understood: the business context; the contents; the individual values; the distributions, etc. Knowing the dataset and the particular business challenge helps the data scientist select the perfect algorithm for converting the data into information and actionable insights.
The reason data science can’t be fully automated at the moment is because the granular discussions and decision-making that lead to a workable solution haven’t happened yet. In other words, we’re still waiting for the rulebook to be written.