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Optimizing Data Quality Management Frameworks for Multinational Enterprises: A Cross-Sector Analysis
Authors
-
Folasade Okunlola
New Mexico Black Leadership Council, New Mexico, United States
Abstract
This study examines the practices, challenges, and strategies for sustaining Data Quality Management (DQM) in multinational enterprises. Drawing on insights from qualitative case studies involving 20 data management leaders across industries with high data dependency, the research identifies four key themes central to effective DQM: adaptability of frameworks, executive support, cross-functional collaboration, and fostering a data-centric organizational culture. The findings highlight the critical balance between global standards and regional flexibility, the pivotal role of leadership in aligning resources and strategies, and the importance of embedding collaborative and cultural practices to enhance data quality outcomes. Additionally, the study emphasizes the role of advanced technologies, such as AI and machine learning, in automating and scaling DQM efforts. This research contributes actionable insights for organizations seeking to refine their data governance and offers theoretical implications for understanding DQM in complex, multinational environments.
Article information
Journal
International Journal of Business and Management
Volume (Issue)
3 (1)
Pages
1-9
Published
Copyright
Copyright (c) 2024 Folasade Okunlola (Author)
Open access
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
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