All of a sudden, data is everywhere.
“Data science is booming simply because the rate of data collection is outstripping our ability to train people to analyze it,” Johns Hopkins professor Brian Caffo says. Many companies are eager to harness new data streams to inform business decisions. But how can we collect, analyze, and understand all of the data that’s available? How can we all use data to make better business decisions? The answer, for many organizations, is building more robust data science teams.
The shortage of data science talent worldwide is serious. Burning Glass, which analyzes a database of current and historical job listings, says there are about 2.5 million open job postings for people with data science and analytics skills in the U.S, a number that’s expected to surpass 2.7 million by 2020.
But responding to the data crunch will take more than just quick hiring. In a 2017 report about our current “age of analytics,” authors from McKinsey & Co. warn: “Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.”
How can leaders think holistically about the future of data science inside their organizations? We asked two data science experts for their advice.
When You Recruit, Think Broadly About Data Science as a Discipline
Caffo is a co-creator of the Coursera data science specialization. He encourages employers to think outside the box when it comes to finding and training data science talent. “Data science combines a set of skills that don’t fit neatly into traditional disciplines,” he says. “Data science includes elements of statistics, computer science, domain knowledge and practical hacking skills.” Because of the many talent streams and diverse applications of data science, Caffo says training outlets are still being worked out. In other words, there’s no one-stop shop.
Sears Merritt is head of data science at MassMutual Financial Group. In recent years MassMutual has been a leader in developing data science talent to fuel its business. Merritt says his team starts by finding junior-level people with “a basic level of fluency and competency in STEM,” referring to science, technology, engineering, and math fields. This also applies for more senior talent: MassMutual looks for a diverse set of skills and points of view when building its data science team, and considers people with backgrounds in fields like math, economics and physics.
Train Talent on Your Specific Tools and Focus Areas
Once MassMutual leaders hire employees with foundational skills, they put them through a rigorous and multi-element training program to complement their existing knowledge. Junior employees enter a three-year development program that includes a formal master’s degree program at the University of Massachusetts in a field like statistics, computer science, data science, or biostatistics.
“In the development program, we build skills on a personalized basis,” Merritt says. “If someone comes in with a degree in computer science, they might need more time building up their math skills for machine learning. We mentor and encourage them to build up their coursework in those areas.”
Caffo echoes the trend of hiring talent with STEM skills and providing additional training on the job. He sees employers hiring scientists like physicists or ecologists and training them in the organization’s unique tools and approaches. But, he says, many organizations are wary of building an internal university to accomplish this supplementary training. He predicts increased collaboration and partnerships between companies, universities, and online learning resources to close the gap on training.
Connect Technical Skills to Business Decisions
In order to be successful once they’re working with a business team, it’s important for data scientists to understand the business implications of their analysis. In the MassMutual development program, junior employees complement their academic training with hands-on industry experience. The company’s senior-level data science talent (mostly Ph.D.s with STEM backgrounds) run data analysis projects and delegate tasks to junior employees, mentoring them along the way.
The push for data science talent isn’t going away anytime soon. As Merritt says, “everything we need to predict, forecast, and automate modern IT systems has at, some level, a core data science component.” Leading organizations will double down on recruiting and training data science talent now, to excel into the next decade.
Learn more about Coursera’s courses, specializations, degrees and professional certificates in Data Science.
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