6 Reasons Why Data Science is an Art

PlaceIQ’s Audience Series sets out to highlight the importance of segments in the advertising world. As the pioneer of mobile’s application to location intelligence, and leaders in the mobile audience field, PlaceIQ has the knowledge you need. Key audience experts from each PIQ department — from engineering, to data science, to sales — will tackle a new topic each week to give a 360-degree view on this vast, ever-changing industry.

If Michelangelo were alive today, what would he do for a living?

He would be a data scientist, of course.

Sure, crunching massive data sets on multi-thousand core clusters using algorithms that were once the exclusive domain of the scientific elites might not seem like an obvious career choice for the famous Caprese maestro, but I firmly believe that he would make quite the data scientist.

Here’s why: Behind the name, data science is a transformative craft, which shares a number of similarities with art.

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  1. Transformative Synergism: In both disciplines, the final total is much greater than the sum of the inputs. In data science our prime raw material (data) goes through the process of transformation through cleansing, parsing, and normalization, followed by iterative optimizations where incremental value is distilled out of the raw material, and where a final result emerges in the form of new information. As point in case, imagine the workshop of Antonio Stradivari systematically transforming a generic block of wood into a unique piece of acoustic excellence.
  2. Technique and Apprenticeship: Art, like data, is based on mastery of technique. A painter takes pride on technical skill the same way a data scientist takes pride on the techniques he or she can bring to the table. And, in both cases, while fundamentals are learned through formal education, true technical mastery is only developed over periods of time and through hands-on experience. Data organizations are essentially workshops where technique is constantly emphasized, transferred, and codified in the form of best practices, intellectual and proprietary information, and techniques.
  3. Experimentalism and Innovation: As piano technology evolved during the 18th and 19th century, composers of the era quickly assimilated the innovations to the instrument and dutifully reflected these into their music. One can imagine the excitement of a young Beethoven when noticing the expressive range of the new pianos of the time. Data scientists have the same curiosity and adventurous spirit as we constantly assess every new technology that promises to produce improved outcomes and workflows.
  4. Creativity, Imagination and Hacker-spirit: Anybody who has worked on code, models, algorithms, or building data pipelines knows that in our line of work, inspiration and creativity are crucial. Sure, we still need the proverbial 99% perspiration, but if you haven’t got the inspiration, no amount of perspiration is going to get you out of your predicament. An online definition of “hacker” refers to a person “who enjoys the intellectual challenge of creatively overcoming or circumventing limitation.” Creativity is a strategic weapon in the hacker’s arsenal. One story tells us of Paganini occasionally breaking his violin strings during performances to demonstrate his virtuosity. Creatively overcoming these self-imposed limitations earns Paganini the honorary title of “hacker.”
  5. Specialization and Differentiation: Nobody disputes that Leonardo’s art is distinctive. Mozart never said “my music is awesome because it sounds just like Bach’s.” Nobody mixes their Dali’s with their Picasso’s. Every data organization strives to achieve a differentiation in technique and approach in order to produce characteristically distinctive results. Like artists, top data science organizations typically operate within their own niches excellence, reflecting the way we think of and approach a problem, our domain of expertise, as well as the resources we leverage and techniques we bring to the table.
  6. Persistence and Detail-orientation: Those of us who have seen architectural marvels like La Alhambra in Spain have inevitably wondered how many thousands, if not millions, of man-hours were spent creating such extensive and magnificent works. Likewise, data science is done through careful persistence and fastidious attention to detail. The true data scientist will understand that the difference between a tight model and a sloppy one is the belief that no detail is too small to matter and that in the end, this obsessive persistence is what makes all the difference.

We can go on listing many other similarities, but the ones mentioned should illustrate the main parallels between data science and art creation. I have no doubt a latter-day Michelangelo would definitely relinquish his chisel and mallet for some Hadoop and NoSQL.

PlaceIQ’s Audience Series sets out to highlight the importance of segments in the advertising world. As the pioneer of mobile’s application to location intelligence, and leaders in the mobile audience field, PlaceIQ has the knowledge you need. Key audience experts from each PIQ department — from engineering, to data science, to sales — will tackle a new topic each week to give a 360-degree view on this vast, ever-changing industry.