Publications The art of data science

The art of data science

Share this article

A Huge Chunk of Marble!

About 6 meters high, over 7 tons heavy and not of the best quality. A few, well-known artists of the time, the beginning of the 16h century, accepted contracts to turn this enormous chunk of marble into one of the twelve statues which were meant to decorate the buttresses of Florence’s cathedral. None succeeded. Finally, it was a 26 years old genius that turned this massive block of Carrara marble into one of the world’s most admired sculptures.

Data Science and the Fine Arts

On the first sight, data science and sculpturing have little in common. The former uses advanced computers and code to generate insights and knowledge while the other relies on hammer and chisel to create art. But viewed in a different light, quite intriguing similarities are revealed.

Faced with a large raw-database, the data scientist commences his work in a similar way to a sculptor who starts by carefully examining his chunk of marble. Possible cracks and the type and condition of the stone determine the tools and feasibility of the project. However, after this initial check, both data scientist and sculptor need to proceed with great care. Hidden, microscopic cracks in the stone can have similarly devastating effects as unnoticed biases in the dataset or small bugs in the analysis code. While the damage in case of the sculpture is clear to the eye, errors and bugs in a data analysis cause false results that are often hard to identify. It is all about experience, the right tools and the right ideas. “A man paints with his brain and not with his hands.” (Michelangelo Buonarotti). The same holds true for sculptors, I suppose.

Who Said Anything About Creation?

The results can often be surprising! “How could they possibly make this?!” is a question that comes to my mind whenever I admire a sculpture that is made of cold stone and yet looks so realistic, almost alive!

Also the results of an advanced data analysis can lead to astonishment. Newly discovered insights, as for example “three quarter of your customer profiles are wrong”, or the high accuracy of a devised predictive model, for example to predict the number of orders or delivered goods for the coming days, trigger questions like “How did you do this?”, “How could you create these insights?”.

But a data scientist does not create the insights. The ‘created’ insights have always been there, in the data, waiting to be revealed. A data scientist’s task is simply to provide the access.

It is said that when Michelangelo was asked how he was possibly able to create his ‘David’ out of this chunk of marble, he answered: “I did not have to create him. He was always there, in the stone. I just had to remove the marble around him.”

In the end, ‘David’ did not end up on top of Florence’s cathedral but found a more prominent place right in front of Palazzo Vecchio (and since 1873 in the Galleria dell’Accademia), to be admired by Florence’s citizens and tourists alike.

Back to previous page
Get in touch!