Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 42 references
Abstract
Industry 4.0 industrial environments rely on highly interconnected and data-intensive ecosystems in which effective data governance is critical to ensure data quality, security, interoperability, compliance, and controlled data sharing. Although several data governance reference frameworks are widely adopted in practice, they were primarily conceived for generic corporate contexts, and their suitability for industrial settings remains insufficiently assessed. This paper presents a coverage-oriented benchmarking of established data governance frameworks with respect to the specific challenges of industrial data governance. Eleven challenges have been identified in prior literature, operationalised into thirtythree measurable items and evaluated using a five-level Likert scale (0-4) based exclusively on authoritative primary framework documentation.. Item-level scores were aggregated into normalised challenge-level and overall coverage indices, enabling systematic comparison across frameworks. The analysis benchmarks four reference frameworks: DAMA-DMBOK, ISACA-COBIT 5, the Data Governance Institute (DGI) framework, and IBM's data governance framework. Results reveal substantial but non-uniform coverage across frameworks: DAMA-DMBOK achieves nearcomplete coverage, whereas IBM, DGI, and ISACA-COBIT 5 exhibit moderate and heterogeneous coverage, with recurrent gaps in privacy conditions, human-related governance risks, scalability across expanding industrial data ecosystems, and operational rules for cross-boundary data sharing and reuse. The proposed benchmarking approach provides a transparent and reproducible basis for framework selection and adaptation in Industry 4.0 contexts and highlights the need for integrative governance models tailored to industrial data ecosystems.
The findings are that companies that implement organized governance systems record dramatic advancement in the quality of data, compliance rates, and the accuracy in analytics, and the need to incorporate governance frameworks in enterprise analytics strategies to promote sustainable data-driven change is highlighted.
Jessica Rachel Brown· International Journal of App...· 0 citations
The increasing complexity of information technology (IT) systems requires reference frameworks that connect governance, service management, and quality assurance. This scoping review maps 109 core sources published between 1996 and June 2026 to characterize evidence on IT reference frameworks across organizational and sectoral contexts; records from 2026 are treated as partial-year data and are not interpreted as a complete annual trend. The review was structured using the Population–Concept–Context (PCC) framework and reported in alignment with PRISMA-ScR guidance. Peer-reviewed studies and selected grey literature sources were charted to identify research trends, framework families, implementation patterns, integration interfaces, evaluation practices, and evidence gaps. Dominant families included ISO/TQM-oriented quality systems, COBIT and ITIL/IT service management (ITSM), enterprise architecture, sectoral quality assurance, cybersecurity, and emerging AI/data-governance approaches. The findings indicate that integration is concentrated around recurring interfaces among strategic governance, service operation, quality and assurance controls, enterprise architecture, and evidence feedback. However, the mapped evidence remains methodologically uneven: conceptual frameworks, case studies, and reviews dominate, while longitudinal validation and comparable implementation metrics are limited. Emerging AI-related work is treated as an early research direction rather than as mature evidence of effectiveness.
Alejandro Quintero Sánchez, José Ricardo Gómez Rodríguez, Luis Alberto Flores Chaires et al.· Information· 0 citations
The findings provide a conceptual basis and strategic guidelines for future DPP deployment, highlighting the importance of appropriate governance mechanisms to strengthen the DPP’s role as a catalyst for the circular economy.
Xabier Narbaiza Iriondo, Borja Diez-Cañamero, Irantzu Calvo Santamaria· Circular Economy and Sustain...· 0 citations
This paper presents MDI’s Data Governance Transformation (DGT) project, an already-in-use data governance policy & infrastructure framework developed to support robust implementation of Privacy Enhancing Technologies (PETs), govern rapid AI development, and confront precedent shattering data use in the United States. As government agencies increasingly rely on complex and distributed data ecosystems, traditional data management approaches have proven insufficient to ensure data quality, accessibility, privacy, and interoperability. This framework establishes a cross-functional data governance structure that includes clearly defined roles, a RACI (Responsible, Accountable, Consulted, Informed) matrix, a standardized change control process, and an architecture rooted in medallion-style data layering. It also includes practical guidance on the following: conducting a data inventory, aligning with cloud and privacy requirements, and coordinating with contractors to ensure data portability and reproducibility. By embedding PETs and clear accountability mechanisms, this framework not only supports compliance with regulatory mandates but also enables data-driven decision-making and responsible use of AI. The framework serves as a replicable model for other government and non-government entities seeking to implement or refresh their data governance strategies to meet the demands of modern public service delivery.
J. Pasner· International Journal of Pop...· 0 citations
Recommendations include formulating Big Data governance policies, establishing governance structures, developing standard operating procedures, implementing periodic monitoring and evaluation, and strengthening stakeholder engagement and communication to support structured, integrated, and accountable big data governance.
Ridha Asih, F. Samopa· Journal of social research· 0 citations
Companies now collect more information about the greenhouse gas emissions of what they buy than ever before, yet much of it never shapes a purchasing decision. This review examined why carbon information so often remains unused in procurement and proposed an organisational and data-governance model to close the gap. A doctoral analysis of information processes in sustainable procurement was synthesised with open-access research published between 2021 and 2026, covering data governance, data ecosystems, sustainable procurement and supply-chain carbon accounting. Carbon information emerged as a layered system in which data, digital systems, decision models and organisational structures depend on one another, so that a weakness in any layer disables the rest. Two layers that practice neglects most received closest attention. At the data-governance layer, reliability rested on clear ownership of each figure, harmonised calculation and verification standards, and reporting arranged to protect data quality. The organisational layer demanded more: a deliberate balance between central and local control, standing coordination among procurement, sustainability and technical functions, carbon indicators built into sourcing targets, and staff able to interpret emissions figures. Evidence from public purchasing and from firm-level studies indicated that disciplined procurement governance measurably lowered emissions. The review concluded that the obstacle to acting on carbon information is organisational before it is technical, and that assigning responsibility and governance should precede further investment in measurement tools.
Keywords: carbon information governance, data governance, sustainable procurement, organisational design, data ownership, scope 3 emissions, supplier carbon data
Lukas Büeck, L. Ritter· International journal of res...· 0 citations