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Today, on this beautiful stormy spring day in northeast Ohio, I am contemplating the arrival of another storm that is brewing in the data science and machine learning space. Specifically, I am thinking about a client call yesterday that centered on a topic that has become a frequent theme lately – the role of the Citizen Data Scientist.
“Just what exactly is a Citizen Data Scientist?” you might also be asking.
Gartner defines a citizen data scientist in “Citizen Data Science Augments Data Discovery and Simplifies Data Science” 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. Citizen data scientists are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Today, citizen data scientists provide a complementary role to expert data scientists. They do not replace the experts, as they do not have the specific, advanced data science expertise to do so. But they certainly bring their OWN expertise and unique skills to the process. (See Figure 1.)
Figure 1: What Does a Citizen Data Scientist Look Like?
“Why now? Why all this talk about citizen data scientists today?” you, too, may wonder.
Many forces are aligning for this “perfect storm”, feeding the potential disruptive and transformative power of this emerging citizen data scientist role. Organizations are increasingly prioritizing the move into more advanced predictive and prescriptive analytics. The expert skills of traditional data scientists to address these challenges are often expensive and difficult to come by. Citizen data scientists can be an effective way to mitigate this current skills gap. Technology is a key enabler of the rise of the citizen data scientist now. Technology has gotten easier for non-specialists to use. Analytic and BI tools are extending their reach to incorporate easier accessibility to both data and analytics. Technology developments also include augmented analytics, approaches that incorporate ML capability to automate data preparation, insight discovery and data science (often referred to as AutoML).
Now I have a question for you:
- Are you leveraging citizen data scientists within your organization?
If so, I’d love to hear your stories in the comments below. Who are they, what are their titles and what do they do?
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