Publicação
Enhancing Data Governance in Big Data: A Strategic FiveDomain Approach
| Resumo: | In today’s digital age, extensive data collection through online activities, smart devices, and biometric feeds drives the growth of the big data market while raising significant privacy concerns. This study explores critical factors influencing the development and implementation of data governance strategies in big data environments. It examines how current practices prioritize data management and monetization, often at the expense of privacy and ethical considerations, underscoring the need for robust governance frameworks. The research highlights the role of advanced technologies like Artificial Intelligence (AI), cloud computing, and blockchain in enhancing data governance by improving data credibility and facilitating better data sharing and analysis. Undertaking a systematic literature review following the PRISMA guidelines, this study identifies key drivers, leadership roles, and barriers to effective data governance. It proposes a novel, interdisciplinary, and sector-agnostic framework tailored to big data environments, structured around five key domains: Digital and DataDriven Transformation, Technologies and Analytics, Data Quality, Ethics, Data Protection and Privacy, and Industry Challenges. This framework aims to help organizations balance value creation with the challenges and expenses of managing extensive data by addressing technological, ethical, regulatory, and competitive dimensions. The study significantly contributes to organizations, practitioners, and policymakers by providing strategies for improving data governance. This approach is essential for aligning data governance practices with technological advancements, streamlining data management procedures, protecting data subjects, and fostering business innovation in an increasingly data-driven world. |
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| Autores principais: | Rodrigues, Francisco De Matos |
| Assunto: | Data Governance Big Data Decision-Making Conceptual Framework Systematic Literature Review (SLR) PRISMA SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | In today’s digital age, extensive data collection through online activities, smart devices, and biometric feeds drives the growth of the big data market while raising significant privacy concerns. This study explores critical factors influencing the development and implementation of data governance strategies in big data environments. It examines how current practices prioritize data management and monetization, often at the expense of privacy and ethical considerations, underscoring the need for robust governance frameworks. The research highlights the role of advanced technologies like Artificial Intelligence (AI), cloud computing, and blockchain in enhancing data governance by improving data credibility and facilitating better data sharing and analysis. Undertaking a systematic literature review following the PRISMA guidelines, this study identifies key drivers, leadership roles, and barriers to effective data governance. It proposes a novel, interdisciplinary, and sector-agnostic framework tailored to big data environments, structured around five key domains: Digital and DataDriven Transformation, Technologies and Analytics, Data Quality, Ethics, Data Protection and Privacy, and Industry Challenges. This framework aims to help organizations balance value creation with the challenges and expenses of managing extensive data by addressing technological, ethical, regulatory, and competitive dimensions. The study significantly contributes to organizations, practitioners, and policymakers by providing strategies for improving data governance. This approach is essential for aligning data governance practices with technological advancements, streamlining data management procedures, protecting data subjects, and fostering business innovation in an increasingly data-driven world. |
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