How Has the Citizen Data Scientist Role Evolved?
Ten years ago, the term ‘Citizen Data Scientist’ was coined by the world-renowned technology research firm, Gartner. The term refers to business team members whose expertise and role are not focused on analytics as a primary job function. Using self-serve analytics solutions, these team members can leverage analytics to create models, reports and analysis to collaborate, share and make decisions. Gartner predicted the emergence of this role within businesses as part of the growing importance of data analytics and data-driven decisions within the business environment.
A decade later, it is worth reviewing the status of this role in the business enterprise and within the average organization. Is the Citizen Data Scientist role a standard role within most businesses today? Does a Citizen Data Scientist replace or work independently from a Data Scientist or Business Analyst? Has the Gartner prediction come to fruition?
While there are no current statistics regarding the number of companies currently using a Citizen Data Scientist approach, the trend toward data-driven planning and forecasting is clear. As with many other business trends, the larger organizations usually take the lead. They have the budget and the depth of resources to plan for and deploy changes across the enterprise and to test theories and enforce cultural changes.
Here are some statistics that reflect the growth of the Citizen Data Scientist movement and the supporting technologies that engender this approach:

- Studies reveal that the number of Citizen Data Scientists is growing five times faster than the number of Data Scientists.
- Automation technologies support the growth of the Citizen Data Scientist approach with over 40% of data science tasks automated through augmented analytics and/or machine learning.
- The Machine Learning (ML) market is growing at a compounded annual rate of more than 15%, reflecting the need for data analytics capabilities within self-serve solutions.
- By some estimates, interest in the Citizen Data Scientist role has tripled in the past decade, as medium and small enterprises embrace new, intuitive, more affordable technologies to support the Citizen Data Scientist concept within their organization.
As this concept became mainstream, the industries saw a trend toward increasing data-driven insight while reducing dependence on Data Scientists.
While the Citizen Data Scientist role began as a basic initiative to gather data and create simple reports, today’s Citizen Data Scientists are now using business intelligence (BI) tools and augmented analytics with Natural Language Processing (NLP), machine learning, low-code and no-code platforms and other technologies to leverage limited technical skills and create sophisticated analytics with clear results. Reports, dashboards and data sharing allow team members to create and use data models and to increase data literacy and data democratization.
Team members can use smart data visualization and assisted predictive modeling to gain insight and solve day-to-day problems, advise management and collaborate with other team members to understand trends, patterns, challenges, and opportunities and leverage metrics to make fact-based decisions.
This evolution of the Citizen Data Scientist role within the organization can free Data Scientists to perform more strategic activities without the daily distraction of simple report requests. If and when a particular data model or analytical approach must be refined to be more strategic, the Citizen Data Scientist can work with the Data Scientist to achieve that goal.
Using this approach, the enterprise can empower team members with the tools to analyze data and to use their knowledge of the industry, market, customers and business environment to make decisions and improve results.
When we consider the last decade of Citizen Data Scientist evolution, we see that businesses across all industries are working toward a more data-driven approach to decision-making, and embracing data democracy as a means to improve productivity and the quality of decisions and to reduce re-work and missteps.
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Original Post : What Are Citizen Data Scientists Doing Today?






