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Mastering the art of data science

Creating a data science team is challenging, but we’ve identified tactics to help organizations build an effective program around a skilled chief data scientist.

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Data science is a hot topic in the C-suites of today’s digital age. Executives leading digital disruptions recount tales of valuable insights uncovered through the application of data science to solve complex business problems. And their global audiences often appear spellbound. But executives who want to join the quest for deep, data-driven insights and value need to understand the requirements-and the risks-when building a powerful data sciences program.

McKinsey & Company projects that demand for deep analytical professionals could exceed the supply by 140,000 to 190,000 positions in the United States alone, noting that this supply constraint will be global. The research firm cites the difficulty of producing this type of talent, estimating that it takes “years of training in the case of someone with intrinsic mathematical abilities.”

The demand for data scientists is so great the recruiting company Glassdoor ranks it as the top job in the United States, giving it high marks for both job satisfaction and career opportunities. One recent analysis of LinkedIn’s global database, which many consider representative of the marketspace, found more than 60,000 job openings for data scientists. However, another analysis found only 11,400 professionals worldwide with the required skills. Moreover, while there has been “impressive growth” in the number of data scientist positions, at least 52 percent of all LinkedIn’s self-identified data scientists have earned that title within the past four years.

Mastering the art of data science

These are well-compensated professionals, both expensive to hire and costly to retain. The U.S. Department of Commerce found that, on average, wages were 68 percent higher for workers in data occupations than for all private workers, and data scientists are among the most well-compensated of all data workers.

This supply-versus-demand imbalance creates a risk for executives looking to hire. Insights aimed at solving an organization’s biggest challenges-operational optimization, revenue generation, innovation-require the art and science that a true data scientist can bring to bear; the wrong approach or flawed analysis from an inexperienced or inadequately trained data scientist can have catastrophic consequences.

Hiring a data scientist is just one step in creating a successful data science program capable of delivering beguiling solutions. Achieving those results requires that executives also think about organizational structures, tools, access, and outcomes, too.

We have logged decades as hands-on data scientists tackling strategically significant business challenges, consulting with hundreds of client teams, and training thousands of aspiring data scientists, both inside and outside of IBM. These experiences have led to a healthy set of lessons learned.

In this IBM Institute for Business Value report, we offer our perspective on the key factors you should consider when seeking to create an effective data sciences program–one that centers on a skilled data scientist creating the right amount of data science artistry to deliver those dazzling results.

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Meet the authors

Mark Grabau

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, Chief Data Scientist, IBM North American Financial Services Sector

Dr. Emily Plachy

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, Distinguished Engineer, IBM Chief Data Office