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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?
The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.
Yes, there are many ‘power’ users in any organisation, who are curious to explore data science and predictive algorithms for their business case, but do not have formal education or experience in data science. This curiosity can fuelled with some basic learning, literacy and of course right tools, and this empowerment will lead them to transform to citizen data scientists, which is the only feasible way today to democratise advanced analytics in an organisation.
Data science should not be a ‘black box’. Declaring the Metadata clearly should be a basic requirement.
There are so many self serve Data Science tools around that there is no need for long complex training or programming to do ‘basic data science’. Its now in the hands of everyone who has the basic concept of how to use data. It’s time to leave just the difficult data science jobs to them.
There is a need to bridge the gap between data science and making sure businesses are exploring the right areas with data science and maximizing ROI.
The question is, how much overlap is there with other roles such as the Business Analyst, Business Intelligence Engineer etc.?
I think there are many roles that may fall into this role category
Any business user ( or even IT ) who is NOT from data science background, and who is power user of BI can learn and explore data science on his own with augmented analytics. They can not replace data scientists skills, but they augment the data science role, as business users ( and then citizen data scientists ) can do their hypothesis and prototyping on their own, and then take it to a data scientist to refine it further.
This is a very informative discussion about Data Science
Thanks for sharing
Citizen Scientists in general have contributed a lot to discoveries in many fields. The early “scientists” of Victorian / Eduardian England were more wealthy dilettantes with a curious mind or poor working people with unmatched observational and analytical skills who found mentors, patronage and employment.
I like the article
Hi, We have seen lots of our clients’ organizations actually move from to citizen model. The one real advantage that citizen ds people have is the business intimacy. On the other hand the bottleneck for them is the reliance on somebody else preparing the core data sets every time and infrastructure. That’s why there was so much grey IT for data analytics in different departments. Especially around Hadoop based environments. As you say, we’re ready for perfect storm. We see it every. Every client.
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It IS important! I’m glad it is helpful for you.
Thanks for the wonderful article Carlie. In our organization, we have Business Analysts play the role of citizen data scientists.BA’s have good functional knowledge and colloborate with data scientists for specific analysis.
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