Today’s data scientist shortage won’t go away soon, yet companies need them more than ever to leverage the value of big data. The solution is to build teams of data scientists instead of seeking soloists.
What is a data scientist?
As computer scientists and programmers, they design the intricate models, algorithms and visualizations that can help companies distil insights from huge volumes of chaotic data. Data scientists also do the work of engineers. They acquire external data sets to supplement internal data, and often manage the data they use and maintain the systems which host it. And as their companies’ top experts on generating analytical insights, they guide, train and sometimes manage other quantitative professionals, and help general managers understand what they need to know about big data-era analytics.
Data scientists are in high demand, but research by the Accenture Institute for High Performance has found the world is facing a severe shortage. There is simply not enough PhD talent to fill the jobs. The shortage is especially severe in the U.S. where 80 percent of new data scientist jobs created between 2010 and 2011 have not been filled, according to our analysis. And the shortage is getting worse. One reason is that data scientists require a scarce combination of skills. They must master advanced statistical and quantitative methods and tools, along with the new computing environments, languages and techniques for managing and integrating large data sets. Data scientists must also possess industry knowledge and business acumen to create models and solve real-world problems. And they need excellent communication and data visualization abilities in order to explain their models and findings to others. That combination is hard to find.
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