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Data Science Automation Enables Complex Analytics

In a world of growing IT data that must be interpreted as meaningful business insights, enterprise organizations demand better tools to enable them to achieve their commercial goals. Cognitive computing can help to tackle this huge challenge, by augmenting human analysis.

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 the latest worldwide market study by Gartner.

What's a 'citizen data scientist' role in this equation? It's a person who generates models that use advanced diagnostic analytics, or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and complex analytics.

Data Scientist Tools Market Development

Citizen data scientists can bridge the gap between mainstream self-service analytics, by business users and the advanced analytics techniques of data scientists. They're able to perform sophisticated analysis that would previously have required more expertise -- now they can utilize advanced analytics, without having the skills of a real data scientist.

With data science continuing to emerge as a key differentiation 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 people 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."

Gartner analysts believe that 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.

Outlook for Data Science Simplification

Therefore, citizen data scientists will surpass professional 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.

According to the Gartner assessment, 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.

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