Data Swagger: A Systemic Approach to Train, Motivate and Engage Data Savvy Employees

Abstract

The relevance of data literacy has increased substantially over the past three decades. When trained well, data-literate employees at all levels can make data-driven decisions, improving the overall performance of their organization. Utilizing Transformative Learning Theory (TLT) and Experiential Learning Theory (ELT), this paper proposes a systematic data education framework for increasing data literacy across organizations. Focusing on the needs and experiences of non-expert end-users, this model proposes the following four learning strategies in data literacy training design: experiential data training, critical incident reflection, rational open discourse, and autonomous experimentation. To inform this model and further investigate barriers to data literacy in organizations, interviews were conducted with individuals from two different data analytics units in the U.S. Department of Defense. This research provides key insight and practical suggestions for developing and improving data literacy training programs.