More Than 40% of Data Science Tasks Will Be Automated by 2020
More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists, according to Gartner. They define a citizen data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.
According to Gartner, citizen data scientists can bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists. They are now able to perform sophisticated analysis that would previously have required more expertise, enabling them to deliver advanced analytics without having the skills that characterize data scientists. With data science continuing to emerge as a powerful differentiator across industries, almost every data and analytics software platform vendor is now focused on making simplification a top goal through the automation of various tasks, such as data integration and model building.
"Making data science products easier for citizen data scientists to use will increase vendors' reach across the enterprise as well as help overcome the skills gap," said Alexander Linden, research vice president at Gartner. "The key to simplicity is the automation of tasks that are repetitive, manual intensive and don't require deep data science expertise." Linden said the increase in automation will also lead to significant productivity improvements for data scientists. Fewer data scientists will be needed to do the same amount of work, but every advanced data science project will still require at least one or two data scientists.
Gartner also predicts that citizen data scientists will surpass data scientists in the amount of advanced analysis produced by 2019. A vast amount of analysis produced by citizen data scientists will feed and impact the business, creating a more pervasive analytics-driven environment, while at the same time supporting the data scientists who can shift their focus onto more complex analysis. The result will be access to more data sources, including more complex data types; a broader and more sophisticated range of analytics capabilities; and the empowering of a large audience of analysts throughout the organization, with a simplified form of data science.