Data analytics is becoming mission-critical to more and more businesses. One of the biggest challenges they face: recruiting data scientists.
“There are very few data scientists out there passing out their resumes,” LinkedIn co-founder Allen Blue said. “Data scientists are almost all already employed, because they’re so much in demand.”
That demand is no longer confined to the high-tech and software realms, Blue said. The past three years have seen “massive growth — 15 times, 20 times growth” in data science-based jobs in sectors like education, marketing and manufacturing. There’s a widening gulf between the needs of organizations and the abilities of job candidates to fulfill those needs.
How are firms grappling with the hiring obstacles? Blue, along with leaders from finance, health care, telecommunications and other industries, shared their insights at “The Future is Now: Closing the Data Analytics Skills Gap,” a town hall sponsored by Wharton Customer Analytics and WorkingNation. WorkingNation is a nonprofit campaign founded by philanthropist and venture capitalist Art Bilger that studies and reports on structural unemployment and the jobs skills gap.
Delivering the keynote, Blue provided a window into how the field of data analytics arose and why it continues to grow, based partly on his personal experience developing LinkedIn. “It’s hard to believe that just 20 years ago, the whole notion of data science was brand new. I don’t think anybody had even used the term yet.” The increasing popularity of the Internet was the catalyst, he said. Every action an Internet user took was visible and trackable, leading to a huge and continuous volume of captured information. This, in turn, enabled novel types of research. Computer scientists began doing real-time analytics of how people were using particular products.
LinkedIn was founded in 2003, and around 2005 is when “the second half of data science came into play,” according to Blue. This second half involved not merely tracking people’s usage of products, but building new products for them to use. Blue said the effort employed machine learning and algorithm development to predict what information a user would find valuable. An early example of this, he said, was LinkedIn’s “People You May Know” feature: figuring out which other LinkedIn members a user might be familiar with (and be tempted to connect with) from previous jobs, schools and other Internet-documented life experiences.
By 2009, said Blue, LinkedIn had several million users. He and his colleagues came up with yet another angle on data analytics, this one a bit startling: With their vast database of members, they could try to shed light on large-scale economic trends.
“[We had] a great idea: Maybe if we could look at the jobs, the skills they required, the skills people had, and the companies they’ve worked for, we might be able to provide insights about where the Skill gaps are and the best opportunities lie.”
LinkedIn has been engaged in this type of research ever since. One of the trends they’ve tracked involves data analytics job opportunities (so in an interesting twist, they’ve used data science to find out about the growth of data science). Blue reported that data science and machine learning-related jobs, taken together, represent five of the top 15 growing jobs in America today. And there are thousands more jobs in the field than there are qualified professionals to fill them.
Data Analytics: The New Sine Qua Non of Business
At the conference, a panel of business leaders from Morgan Stanley, PwC, Comcast and other organizations discussed the expanding role of data analytics. Tsvi Gal, CTO of infrastructure at Morgan Stanley, emphasized the importance of data science in today’s financial industry. “We [may be] in banking, but we live and die on information…. Data analytics is the oxygen of Wall Street.
