Anybody who has tried hiring a data scientist can attest to the fact that we’re in the midst of a skills crunch of epic proportion.
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Cutthroat competition and sky high salaries are just two signs of the considerable lack of available data science talent. Some folks are trying to fill that by putting automated machine learning (AutoML) tools in front of citizen data scientists, but others warn that it could backfire.
Gartner is credited with coining the title “citizen data scientist” back in 2016 to refer to data professionals who use advanced software like AutoML packages to develop predictive analytic applications. While citizen data scientists’ primary job function typically lies outside statistics and analytics, they’re knowledge of the business and access to new tools turns them into “power users” who can tackle simple and moderately sophisticated analytical tasks that would typically have required the services of a full-fledged data scientist.
Thanks to unsatisfied demand and better AutoML software, the conditions are ripe for a “perfect storm” for the creation of citizen data scientists, Gartner analyst Carlie Idoine wrote in a May 2018 blog post.
“Organizations are increasingly prioritizing the move into more advanced predictive and prescriptive analytics,” Idoine wrote. “The expert skills of traditional data scientists to address these challenges are often expensive and difficult to come by. Citizen data scientists can be an effective way to mitigate this current skills gap.”
Nathan Korda, the director of research at data science platform provider Mind Foundry, says that citizen data scientists are enabling organizations to take fuller advantage of their ever-growing collections of data. In the May 2019 Datanami article “The Rise of the Citizen Data Scientist: How Humanized Machine Learning Is Augmenting Human Intelligence,” Korda defines citizen data scientists as:
“Employees [who are] not operating in dedicated data science or analytics roles, who can use a humanized machine learning platform to explore their data and easily deploy models to unlock the value it holds,” Korda wrote. “Thanks to user-centric platforms, current employees can enjoy access to machine learning technology without the need for specialist training.”
Organizations seeking data science capabilities have turned to a new wave of capable AutoML tools to jumpstart their initiatives. Forrester recently ranked DataRobot and H2O.ai as the two leading AutoML providers, with other firms like dotData providing solid functionality in a fast-growing sector.
Thanks to better software and a continued shortage of data scientists, Gartner estimates that by 2020, 40% of data science tasks will be automated through the use of AutoML tools and data science platforms. The Connecticut firm has also stated that the ranks of citizen data scientists are growing 5x faster than full-fledged data scientists.
Clearly, there is momentum behind the citizen data science trend. But not everybody is hopping on this bandwagon. One person who is expressing caution about it is Nick Elprin, the co-founder and CEO of Domino Data Lab, a San Francisco-based provider of a data science platform. […]