Project management has become a paramount tool in project development, regardless the sector or size of a project, Project Management Methodologies (PMM) is employed to enhance the probability of meeting the project targets. According to the recent trend of using the agile approach on different types of projects, it becomes obvious to say that there are two opposite flows-traditional and agile project management approach. The paper starts with a presentation of the traditional approach and the agile movement. It provides an overview of different methodologies of the two sides. The literature review shows a comparison between the two approaches and what the main characteristics of each methodology are. This study aims in the first instance to provide some criteria to choose the appropriate approach for a project. In the second instance, through a bibliometric analysis to show the synergy between the two movements.

In today's environment, where data is a critical asset for decision-making, the flexibility and responsiveness of data strategies are crucial. However, many organizations face challenges in creating an integrated approach that spans the entire data lifecycle, from inception to insights. To address these challenges, the article presents a "Unified Data Framework" aimed at enhancing the efficiency of data handling across various stages—management, consumption, provisioning, processing, and transfer. By proposing a holistic strategy that encapsulates all aspects of the data process, this framework serves as a pivotal contribution to the field. It is specifically designed to bridge the gaps between disparate data functions and streamline the flow of information. The culmination of these elements fosters an environment where data is not only secure and well-governed but also readily accessible and actionable, thereby enhancing decision-making and enabling a competitive edge in the marketplace.

In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises.

Smart supply chains, enabled by Industry 4.0 technologies, are increasingly recognized as key drivers of competitiveness, leveraging data across the value chain to enhance visibility, responsiveness, and resilience, while supporting better planning, optimized resource utilization, and agile customer service. Effective data management has thus become a strategic capability, fostering operational performance, innovation, and long-term value creation. However, existing research and practice remain fragmented, often focusing on isolated functions such as production, logistics, or quality, the most data-intensive and critical domains in smart manufacturing, without comprehensively addressing data acquisition, storage, integration, analysis, and visualization across all supply chain phases. This article addresses these gaps through a systematic literature review of 55 peer-reviewed studies published between 2020 and 2025, conducted following PRISMA guidelines using Scopus and Web of Science. Contributions are categorized into reviews, frameworks/models, and empirical studies, and the analysis examines how data is collected, integrated, and leveraged across the entire supply chain. By adopting a holistic perspective, this study provides a comprehensive understanding of data management in smart manufacturing supply chains, highlights current practices and persistent challenges, and identifies key avenues for future research.

Healthcare organizations increasingly require secure, governed and AI-ready data pipelines capable of handling heterogeneous and sensitive data sources. This study aims to design and evaluate a unified DataOps reference architecture that operationalizes the full data lifecycle through a governed Medallion Lakehouse model. Methodologically, the proposed architecture integrates data-centric CI/CD, Infrastructure as Code, workflow orchestration, governance and metadata management, monitoring, and explicit promotion contracts across Bronze, Silver, and Gold layers. The framework was implemented and evaluated in a controlled healthcare testbed using approximately 3.5 GB of multi-source clinical data over a 25-day workload. The findings show that the proposed architecture achieved a DataOps Operational Excellence Index (DOEI) of 0.92, an ingestion throughput of approximately 100 MB/s, a data quality score of 97.87% and a 72% reduction in infrastructure provisioning time, from 3 hours to 50 minutes. The main novelty of this work lies in combining a governed Lakehouse-based DataOps architecture with explicit promotion contracts and a composite benchmarking index for assessing operational maturity. This improvement provides a reproducible, auditable, and scalable framework for secure data operations in regulated environments such as healthcare.