Skip to content
Research Article Open access CC BY 4.0

Data Governance for Emerging Technologies: A Conceptual Framework for Managing Blockchain, IoT, and AI

Iveren M. Leghemo, Chima Azubuike, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu

Journal of Engineering Research and Reports · pp. 247–267 · Published 18 Jan 2025

10.9734/jerr/2025/v27i11385

Abstract

As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.

Data governance blockchain internet of things (IoT) artificial intelligence (AI) data integrity data privacy security ethics compliance emerging technologies

Cited by 18

Smart Printing

M. Maniraj, S. M. Raj Kumar · Modeling, Analysis, and Control of 3D Printing Processes · 2025

A review of generative AI in aquaculture: Applications, case studies and challenges for smart and sustainable farming

Waseem Akram, Muhayy Ud Din, Lyes Saad Saoud · Aquacultural Engineering · 2026

Data Harmonization as a Keystone for Data Spaces: Challenges, Techniques, and Future Trends

Josu Diaz-de-Arcaya, Asier Garcia-Perez, Lander Bonilla · 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech) · 2025

The Importance of AI Data Governance in Large Language Models

Saurabh Pahune, Zahid Akhtar, Venkatesh Mandapati · Big Data and Cognitive Computing · 2025

Transforming Food Systems with Artificial Intelligence: Challenges, Governance, and Pathways for Sustainable Integration

Azfaralariff Ahmad, Maizura Murad, Uthumporn Utra · Food Reviews International · 2025

Showing 8 of 18 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

0

Page views

0

PDF downloads

0

Outbound clicks

18

Citations

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

No views recorded yet.

Traffic sources

Referring site, by host.

No traffic recorded yet.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.