Citizen data scientists are going to allow data scientists and the companies for which they work to leverage themselves more. While data scientists understandably “don’t like to give the keys to the Ferrari to the 16-year-old,” as one such expert said, they’ll have to enable self-service.
A New Role for Business Users
These business users – who now are being called citizen data scientists – are beginning to find some things that need to be validated and investigated. That brings the data scientist back in place as a trusted adviser as a predictive model may need to be built to put into production or into an application to ultimately improve the business, according to Lisa Kart of Gartner, Inc.
“If you want to expand your circle of influence, you have to enable some of this self-service,” said Kart at the end of her session on “Key Trends in Advanced Analytics” (subscription or fee required) at the Gartner Business Intelligence & Analytics Summit.
Analytics may not be the primary job responsibility of these citizen data scientists. But Kart said she’s seeing more people emerge from within companies to fill these roles as the tools become easier to use and self-service is the norm.
How an organization can leverage the power of advanced analytics is through finding these data scientists within their company. And an organization looking to get started with advanced analytics doesn’t have to go it alone.
“Please if you are looking to get into predictive analytics remember that you don’t have to build it all yourself, especially to get started,” Kart said. “You want to also look for packaged analytic applications, embedded analytics and even service providers.”
If an embedded analytics platform or other service can handle a problem for the organization, that makes it a very quick solution to the problem.
These platforms and workbenches have features such as templates or even a full package application for a business user. The analytics marketplace offers pre-built applications, APIs, data and models. That includes an analytics ecosystem provided through a vendor or third parties, such as the Microsoft Azure Machine Learning Marketplace.
The value includes speed to market, scalability and ease of deployment.
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