Stefanie Scholz

doi:10.19080/ASM.2023.08.555757

Mini Review

Towards a Democratization of Data Knowledge in Companies: The Concept of the Citizen Data Scientist

Stefanie Scholz*

Professor of Data Science in Social Economy, SRH Wilhelm Loehe University of Applied Sciences, Fuerth, Germany

Submission: July 21, 2023; Published: August 03, 2023

*Corresponding author: Stefanie Scholz, PhD, Professor of Data Science for Social Economy, SRH Wilhelm Loehe University of Applied Sciences, Merkurstr. 19, 90763 Fuerth, Germany, stefanie.scholz@srh.de

How to cite this article:Stefanie S. Towards a Democratization of Data Knowledge in Companies: The Concept of the Citizen Data Scientist. Ann Soc Sci Manage Stud. 2023; 9(2): 555757. DOI: 10.19080/ASM.2023.09.555757

Abstract

In recent years, the emergence of the citizen data scientist has transformed the landscape of data analytics and decision-making within organizations. The concept of the Citizen Data Scientist is based not only on the availability of data but also on the democratization of analytical capabilities. The ability to analyze data and gain insights autonomously should not be restricted to experts alone, but should be made accessible to a broader group according to this perspective. This democratization is crucial within organizations as well, in order to decentralize and expedite data-driven decision-making for different departments. By utilizing intuitive data analysis tools, individuals without specialized training or extensive experience in data analysis can be empowered to analyze data for their specific needs and gain insights without relying on the availability of data specialists. In order to successfully implement the concept of citizen data science within companies, it is crucial to consider and address the challenges and risks, despite the numerous potentials and benefits it offers.

Keywords: Citizen Data Scientist; Data-Driven Insights; Democratization; Domain Expertise; Decision-Making; Employee Empowerment

Introduction

With the exponential growth of data, organizations face a continuously growing need to harness its potential for informed decision-making. The concept of the citizen data scientist arose as a response to this demand, aiming to empower employees who possess domain expertise but lack traditional data science or IT and engineering backgrounds. The term “citizen data scientist” was introduced 2015 by Gartner [1], a leading research and advisory company. Gartner recognized the untapped potential of non-experts to bridge the gap between data science teams and domain experts within different business units (Figure 1).

The citizen data scientist concept enables organizations to democratize data analysis by empowering a broader range of employees [2]. Furthermore, it offers a crucial advantage by enabling the utilization of diverse perspectives by fostering data literacy of employees. When data analysis is confined to a specific cohort of trained experts, a potential pitfall of narrow focus is likely, as these individuals tend to tackle problems from a homogeneous standpoint. By empowering a broader spectrum of employees to actively interact with data, organizations can leverage a broader array of perspectives, facilitating the emergence of diverse insights and ideas. This, in turn, fosters the development of innovative solutions and enhances the overall quality of decision-making processes [3]. By leveraging the expertise of employees who possess domain knowledge, companies can expedite data analysis processes. Citizen data scientists can independently explore datasets, identify patterns, and generate insights, leading to faster decision-making and improved productivity [4]. Engaging citizen data scientists reduces reliance on dedicated data science teams or external consultants. This not only lowers costs but also allows organizations to scale their data-driven initiatives more efficiently. Citizen data scientists bridge the gap between technical and business functions, promoting collaboration and knowledge sharing. By combining domain expertise with data analytics skills, they facilitate cross-functional communication, leading to more effective problem-solving [2]. With citizen data scientists distributed across various departments, organizations become more agile and adaptable to changing market dynamics. They can swiftly respond to emerging trends, make data-informed decisions, and gain a competitive edge [5] (Figure 2).

When embarking on the implementation of citizen data science within a company, there may be potential pitfalls and challenges that need to be addressed. Citizen data scientists may lack formal training in data analysis, statistics, or programming. This could lead to misinterpretation of data or biases in analysis, potentially impacting decision-making. Furthermore, the involvement of citizen data scientists increases the access to sensitive data, raising concerns about data security, privacy, and compliance. Organizations must establish clear guidelines and protocols to mitigate these risks. Without stringent oversight, the analysis conducted by citizen data scientists might lack consistency and quality. Establishing processes for validation and peer review is crucial to maintain accuracy and reliability. As the number of citizen data scientists increases, organizations must ensure scalability and proper governance. A centralized framework, training programs, and clear roles and responsibilities are essential to maintain consistency and prevent chaos. The concept of the citizen data scientist holds tremendous potential for organizations seeking to harness the power of data. By empowering employees with domain knowledge, companies can unlock new insights, enhance decision-making, and drive innovation. However, to reap the full benefits, organizations must address the associated risks and challenges, implementing robust governance and training programs to ensure the integrity and security of data-driven initiatives.

Company culture requirements for establishing the citizen data scientist concept

Companies should promote a culture that values data-driven decision-making: A data-driven mindset. The encouragement of employees at all levels is important in order to rely on data and insights when making business decisions, rather than relying solely on intuition or past experiences [6].

In the pursuit of data science projects, it is imperative to create a collaborative environment that brings together employees from diverse departments and backgrounds. The emphasis should be on fostering cross-functional teams, allowing them to harness their distinct skills and perspectives to address intricate challenges [7]. Continuous learning must be prioritized, and ample opportunities should be provided for employees to acquire data science skills. This can be achieved through the implementation of training programs, workshops, and various resources, which serve to enhance employees’ analytical capabilities and cultivate a culture of learning [7]. To facilitate the exploration and analysis of data without excessive reliance on technical experts, it is essential to ensure the availability of relevant data and user-friendly analytics tools. This can be accomplished by providing self-service analytics platforms and accessible data repositories, enabling citizen data scientists to engage with data effectively.

Establishing effective communication channels is vital to promote knowledge sharing and collaboration among citizen data scientists. Encouraging open dialogue, providing platforms for sharing insights and best practices, and fostering a sense of community among employees contribute to an enriched learning environment. Recognizing and rewarding active engagement in citizen data science initiatives is pivotal. By highlighting success stories and celebrating achievements, incentives can be created to motivate employees to actively participate in and contribute to data-driven projects. Encouraging an environment that supports experimentation and calculated risk-taking is essential for fostering innovation and growth in the field of data science. Employees should be motivated to explore new ideas, test hypotheses, and view failures as valuable opportunities for learning. Ethical data practices should be upheld throughout the organization. This involves promoting a comprehensive understanding of data privacy, security, and compliance among employees [8]. Guidelines and training sessions can be implemented to ensure the responsible handling of sensitive data and maintenance of data integrity in adherence to relevant regulations. Leadership plays a vital role in creating a culture that embraces citizen data science. It is crucial for leaders to demonstrate their commitment to data-driven decision-making, allocate necessary resources, and actively advocate for the adoption of citizen data science within the organization [7].

A diverse and inclusive culture should be fostered, welcoming individuals with various backgrounds and perspectives into citizen data science teams. Embracing diversity can lead to heightened creativity, enhanced problem-solving capabilities, and increased innovation.

Conclusion

The rise of the citizen data scientist has revolutionized data analytics and decision-making in organizations. The democratization of analytical capabilities allows a wider range of individuals to autonomously analyze data and derive insights, beyond the confines of data experts. This democratization is essential for decentralized and efficient data-driven decisionmaking across different departments. By leveraging user-friendly data analysis tools, employees without specialized training can be empowered to analyze data for their specific needs. However, implementing citizen data science comes with challenges and risks that must be carefully navigated. By acknowledging and addressing these obstacles, companies can unlock the full potential of citizen data science while reaping its numerous benefits.