by Angela Guess
Alex Woodie recently wrote in Datanami, “Shakespeare once pondered the nature of names, pointing out that ‘a rose by any other name would smell as sweet.’ For data scientists, the meaning behind the title is not just an epistemological exercise, but a practical problem that has consequences upon that delicate dance between employer and employee. The data scientist shortage is having all kinds of impacts on how organizations approach big data projects. As we explored in the previous story in this series, the shortage is leading many organizations to consider data science as a team sport. That’s the approach espoused by Bill Schmarzo, a VP at EMC, who is encouraging business managers to ‘think like data scientists’ to identify potential metrics and variables that data scientists (and data engineers) can then validate and put into production.”
Woodie goes on, “This approach bears similarity to the rise of so-called ‘citizen data scientists,’ an approach that has been backed strongly by Gartner. Citizen data scientists differ from full-fledged data scientists in that they lack the full suite of skills (math/statistics, programming/computer science, and business/industry experience) that a data science is traditionally expected to have. Citizen data scientists are essentially data analysts who possess light statistical and programming skills, and lean on sophisticated software to fill in the gaps compared to full-fledged data scientists. One supporter of the citizen data scientist movement is Peter Schlampp, the vice president of products for big data analytic solution provider Platfora. ‘We identified years ago we need more data scientists and universities and companies have caught onto this and they’re educating and training people to do this at school and on the job,’ he says. ‘But the gap will remain. Before we get there, we need to do something.’”
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