Much like the citizen developer, citizen data scientists are springing up from within the business analyst community. These are data savvy individuals who are deeply interested in machine learning and stretching themselves to learn deeper data concepts. New tools that are simplifying the entire predictive process, are making this trend possible. Look for the democratization of data science to come as the community of citizen data scientists forms and begins to educate themselves.
In January 2015, Gartner came up with a new term to describe a new breed of people: Citizen Data Scientists. Citizen Data Scientists
Gartner defines a "citizen data scientist" as a person who creates models that use predictive or
prescriptive analytics, but whose primary job function is outside of the field of statistics and
advanced analytics. Gartner introduced the term "citizen data scientists" in "Predicts 2015: A Step
Change in the Industrialization of Advanced Analytics." They are "power users" who will be able to
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perform simple and moderately sophisticated analytic applications that would previously have
required more expertise. They often reside in the lines of business and have deep domain expertise.
New tools will also make highly skilled data scientists more productive, enabling them to produce
more analysis in the same amount of time. Organizations will still require skilled data scientists for
demanding, deep analytic applications. Such data scientists will also have a growing role to play in
mentoring citizen data scientists and validating some of their results.
‘Demand for deep analytical talent in the United States could be 50 to 60 percent greater than its projected supply by 2018’ – McKinsey Global Institute
EMPLOYEES WHO AREN’T DATA SCIENTISTS OR ANALYSTS SHOULD BE ABLE TO ASK QUESTIONS OF THE DATA BASED ON THEIR OWN BUSINESS EXPERTISE AND QUICKLY AND EASILY FIND PATTERNS, SPOT INCONSISTENCIES, EVEN GET ANSWERS TO QUESTIONS THEY HAVEN’T YET THOUGHT TO ASK
Visualization-based data discovery solutions that offer highly interactive and graphical user interfaces, are built on in-memory architectures, and are geared toward addressing business users’ unmet ease-of-use and rapid deplo
yment needs. These solutions typically enable users to explore data without much training, making them accessible by a wider range of employees than traditional business analysis tools.
Making Big Data accessible to more users across the enterprise in a way that’s easy and approachable isn’t an end in itself. The real benefits come from the insight revealed by analysis of Big Data, and how an organization capitalizes on those answers. In the IDG Research survey, of those organizations that are considering using data visualization, 77% of respondents cite improved decision making as a top benefit, while 45% cite better ad-hoc data analysis and 44% cite improved collaboration.
