Citizen Data Scientists: 7 Ways To Harness Talent - InformationWeek

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Data Management // Big Data Analytics
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Lisa Morgan
Lisa Morgan
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Citizen Data Scientists: 7 Ways To Harness Talent

A new role is emerging to deal with the ongoing shortage of data scientists. Learn more about these new power users and find out how organizations can cultivate more of them.
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(Image: RyanMcGuire via Pixabay)

(Image: RyanMcGuire via Pixabay)

The worldwide shortage of data scientists won't end anytime soon. To try to compensate for the shortage, data discovery solutions are automating tasks that have traditionally been done manually by a data scientist, statistician, or other analytics expert. The confluence of trends is giving rise to a new role that Gartner calls a "citizen data scientist."

A recent Gartner report defines a citizen data scientist as "a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics." It could be a line-of-business role, a business analyst, or a member of the business intelligence or IT team. The defining trait is that statistics and analytics are secondary in the role.

Not everyone in an organization will become a citizen data scientist -- at least by Gartner's definition. By that standard citizen data scientists are power users. The new role does not threaten those of data scientists, data analysts, or business analysts; it complements them. And in fact, citizen data scientists necessarily have to work with other roles to derive the most value from analytics.

Like anyone else in an organization, citizen data scientists need the right technology to do their jobs. In this case, that's one of data-discovery offerings that automate parts of complex processes such as data preparation and pattern identification.

[Read about the challenges of data misinterpretation.]

As advanced analytics capabilities become available to more people, companies will have to ensure they have the governance in place to make it work, which includes software enforcement of governance policies. According to Gartner, by 2018 the multiple styles of data discovery available today -- smart, governed, Hadoop-based, search-based, visual-based, and graph-based -- will converge as their unique capabilities become requirements. And the convergence is already under way.

Click through the following pages to learn seven ways companies can prepare for the coming wave of citizen data scientists.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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User Rank: Moderator
7/31/2015 | 10:29:40 AM
Re: Current workforce
Well, let me clarify:  There are those who have the desire and chops to grow into an actual data scientist role and those who are better suited to a power user role (citizen data scientist).  There are courses at colleges/universities and other types of training aimed at different audiences.  

Your points are good ones.
User Rank: Ninja
7/31/2015 | 9:30:30 AM
Current workforce
You mentioned educating data scientist at the university level. I'm willing to bet there are more than a few people already in many companies that have a working knowledge of statistics that would be happy to take a few more courses to fill in the data scientist gaps for the extra pay involved. Don't discount the people you already have that could be trained up.
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