Data Swagger: A Systemic Approach to Train, Motivate and Engage Data Savvy Employees
Files
Date
2022-01-04
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
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.
Description
Keywords
Business Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management, data literacy, data savvy, education, training
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.