The Growth and Change of the Analytics Community
This article is the start of a new series exploring the changes of the analytics community and the changes from descriptive analytics to the push for prescriptive analytics.
Over the last decade, the analytics community has been at the forefront of the media and been referred to as the sexiest, most desirable, and ever-changing community (well career, but who’s counting). In the US alone, there are between 200,000 and 1 million analytics professionals with Data Scientists making up less than 10%.
Beyond only growing our numbers, the community has changed from being statisticians and analysts in the financial industry to a group that effects nearly every industry across the world. No longer does the community live solely in the ivory towers of Goldman and Sachs or Morgan Stanley, today we clamor for companies that didn’t exist 20 years ago such as Data Robot and Quantum Black. These new companies came with a different culture from those that came before, being a suit in a sea of black doesn’t entice us compared to T’s and jeans.
"These data scientists shaped the culture of the companies they joined."
What makes us different
That culture change stems from us being unique, not cookie cutter or made from an assembly line. This comes from not having a set path within data science. The first data scientists weren’t even known as data scientists. They went Wall Street and tended to graduate from the same schools with the same training as their colleagues on the street, molding them into a uniform shape that matched their work environment. These early Data Scientist were known as Quants and they wore suits and looked like traders, but this wouldn’t last.
Meanwhile, across the United States, on the sunny shores of California, a data revolution was beginning. This revolution effected more than just how data was thought of, but the people that were using it. Hundreds of data science startups emerged in a short period of time and they didn’t have the suit culture of the Quants. Being in a startup, these data scientists shaped the culture of the companies they joined based on where they were from and their background. Only recently have education programs focusing on applying analytics to business issues have become more common. This leaves the rest of us having a wide backgrounds from PhD academics crossing over to those without any college degree, just determination to learn on their own. We have built a community of artisans that aims to reshape the world.
From Artisans to Assembly Lines
Given the lack of a uniform background, the specialized skills, and most models being handmade, the best term for data scientists would be artisans. Artisans are highly skilled individuals that make one off works of art akin to many of our models today. Take the Kentucky Rifle for example, a muzzle loading rifle used in the revolutionary war. Even if an artisan made two of those rifles back to back, the parts wouldn’t be interchangeable between the two, just as two models cannot be interchanged between data science projects, at the very least without retraining the model.
Similarly, how the stonemasons reshaped the world with the castles and cathedrals of Europe, our community has already reshaped the world in ways that were only dreamed of in Sci-Fi. Today, you get personalized recommendations for shopping, on an item that will be delivered for free on the same day, for a device that tracks your biometric information and gamifies your health and fitness.
Each of these steps; recommendations, logistical enhancements, and the gamification of health were custom made from artisans in the fields of computers, statistics, and business. Just like how the manufacturing community grew from artisans and apprenticeships, creating one off saddles and carriages, to assembly lines creating thousands of Ford Model T’s a day, our community will grow from artisan Data Scientists to an analytics assembly line, producing a constant stream of insights.
This article is Part 1 of 5 so join me next time as we look at how businesses became data informed by transforming some of these artisans into assembly line workers for data.
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