We Were Data Scientists Before Data Science Was Cool: New Challenges for the Profession

Suddenly, being a data scientist is cool. And in high demand.

Why? Because these days, data makes the world go round. Nearly every industry in our economic ecosystem is clamoring for it.

If a company, no matter the industry, is not using Big Data to chart and forecast customers' journeys, better connect with them, ferret out their wants and needs before they even know what they are (thank you, Netflix, for creating that perception in people's minds), and otherwise using the numbers to enhance the customer experience, it will be left in the dust by competitors that do.

The increased demand for data in all sectors of the economy has created a boom in the data science field. According to Forbes magazine, the fastest-growing jobs in the country today are data scientist, machine learning engineer and big data engineer. In the blink of an eye, every company needs people who can make sense of data. LinkedIn conducted a survey and found there are 6.5 times as many data scientists working today than there were just five years ago. For machine learning engineers, that number jumps to 9.8.

"The field has exploded within the past four or five years," says Nuray Yurt, head of enterprise data science at Novartis. But, she points out, while the need for data pros continues to ramp up, which is a good thing on many levels for the profession, it also brings with it some challenges for the data scientists themselves.

Challenges for Data Scientists Today

The situation can be loosely compared to the disruption the corporate training field went through back when the internet was first starting to change the way every company on earth worked. People got into the training profession because they liked teaching in front of a classroom, which is where the bulk of training happened pre-internet. But very soon after the screech of dial-up technology began connecting every desk in every office to the World Wide Web, someone got the idea that training should happen online, so trainees could sit at those very desks and get the knowledge they needed on their own schedule. Suddenly, trainers had to learn an entirely new skillset — creating online learning modules. It was not what they signed up for, but it quickly became an essential part of the job.

Data scientists are finding themselves in a similar predicament today. The nuts and bolts of analyzing data are always evolving, but the skills to do the job, like analyzing statistics, computer knowledge and business knowledge, remain the same. What's new for data scientists are the so-called soft skills that are becoming necessary parts of the job.

"Data scientists need to be curious, open minded, quick learners and have the right personality fit now," Yurt explains.

Communication skills are a vital part of that. Why? Because industries that are newly reliant on data, like sales, customer service and hospitality, are hiring data scientists to help them make sense of it all. And, gently put, the people who run those companies are not data scientists nor have they ever had one on staff. As Yurt notes, everyone now knows what to do with data, but few know what it takes to glean that data, analyze it and translate it into actionable goals and strategies for companies to implement. So, data scientists are suddenly put into the position of emerging from their offices where they've been happily crunching numbers on their own and explaining to higher-ups what the data science actually means, in language they can understand.

The temptation may be to "dumb down" the explanation, but Yurt says that's a mistake.

"The challenge for data scientists today is being able to communicate complex concepts to people who don't understand them without diluting the complexity," she says. That last part is the key. People in industries new to data need to understand the complexity of the process, or it diminishes the data science field as a whole. It also puts funding and potentially jobs at risk if people don't entirely get the fact that analyzing and interpreting data is a science that Hal from accounting wasn't trained for.

"We need to communicate why and how what we do makes a difference," she says.

Another challenge for data scientists is the need to be more open minded. "We need to be OK with change," Yurt says. "Our jobs won't be the same as they always were, and we need to be OK with that."


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