Data Governance Principles, Decision Domains and Organizational Structures
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 4074
Special Issue Editors
Interests: cyber security domains of network and system security; ethical hacking/penetration testing; network monitoring; misuse detection and authentication; applications of cyber including eSafety (especially children) and security education
Interests: different aspects of security, privacy and trust practices to address emergency events such as the COVID-19 outbreak and other e-health measures; data governance and big data applications; Internet of Things and data quality; context-aware access control; data sharing and privacy; security and AI; ransomware detection and defense; IoT security; cloud/fog security
Special Issues, Collections and Topics in MDPI journals
Interests: data governance and integration; service-oriented computing; infrastructure as code; machine learning operations; data engineering; cloud computing; deployment automation; semantic web technologies
Interests: cybersecurity; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are currently living in a data-driven world and dealing with big data and Internet of Things. Data-intensive products and services aim to turn big data into a value or strategic asset for organizations. However, the inherent risk and cost of storing and managing a massive amount of data undermine the value created by such products and services. Consequently, organizations are required to adopt an appropriate data governance program to establish the necessary policies and structures in order to strike a balance between value creation and risk and cost. This research topic explores the data governance in detail, focusing on data governance principles, decision domains, and organizational structures. Data governance challenges, opportunities, and practices for big data and Internet of Things (IoT) domains must be further explored. It is also necessary to diagnose industrial big data applications and products whose data need further governance.
Dr. Paul Haskell-Dowland
Dr. A.S.M. Kayes
Dr. Indika Kumara
Dr. Mohiuddin Ahmed
Guest Editors
Manuscript Submission Information
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Keywords
- data governance
- data quality
- data lifecycle
- data governance structure
- big data
- IoT data
- data freshness
- data trustworthiness
- data reliability
- data governance principles
- decision domains
- organizational structure
