Automation predicted for 40% of data science tasks

Gartner predicts that over 40% of data science tasks will be automated by the year 2020.

This will result in greater productivity and usage of data and analytics by citizen 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."

According to Linden, 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 defines 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.

It is thought that 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 characterise 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.

"Most organisations don't have enough data scientists consistently available throughout the business, but they do have plenty of skilled information analysts that could become citizen data scientists," said Joao Tapadinhas, research director at Gartner.

"Equipped with the proper tools, they can perform intricate diagnostic analysis and create models that leverage predictive or prescriptive analytics. This enables them to go beyond the analytics reach of regular business users into analytics processes with greater depth and breadth."

According to Gartner, 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 organisation, with a simplified form of data science.

"Access to data science is currently uneven, due to lack of resources and complexity — not all organisations will be able leverage it," said Tapadinhas.

"For some organisations, citizen data science will therefore be a simpler and quicker solution — their best path to advanced analytics."

Image credit: ©stock.adobe.com/au/Alphaspirit

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