Metadata-driven data models have become a crucial facilitator in the advancement of self-service business intelligence (BI) platforms. Organisations can utilise metadata to develop adaptable, scalable, and user-centric data models that enable end users to...
Organizations must obtain insights into often fragmented and isolated data assets and eliminate data silos to properly leverage data as a strategic resource. Data catalogs are an increasingly preferred approach for achieving these objectives. Despite the...
Large Language Models (LLMs) are rapidly transforming multiple sectors, including healthcare, finance, and cybersecurity, by enabling advanced data-driven insights and automation. However, the scale and complexity of LLMs introduce significant challenges in...
The rapid proliferation of Artificial Intelligence (AI) across industries has placed data quality at the forefront of organizational priorities. High-quality data is essential not only for the accurate training of AI models but also for ensuring ethical,...
As organizations increasingly leverage Artificial Intelligence (AI) to foster innovation and improve operational performance, the availability of high-quality, well-documented, and easily accessible data has become a decisive factor for success. This paper...
Real-time data processing has emerged as a crucial tool for contemporary enterprises, allowing instantaneous analysis and response to perpetually created data. This study examines the significant influence of real-time data processing on corporate...
Artificial intelligence is reshaping financial analysis, yet the value of advanced algorithms depends heavily on the quality of the data and governance systems surrounding them. This review examines how artificial intelligence strengthens data governance...
The rapid advancement of Large Language Models (LLMs) has significantly transformed industries such as healthcare, finance, e-commerce, travel, and cybersecurity by enabling intelligent automation, advanced analytics, and human-like communication. However,...
The recent increase in the use of big data and analytics in various global industries has created pressure on industries to develop reliable and flexible frameworks for data governance. Historically used approaches can be ineffective in addressing the...
Customer Relationship Management systems have become essential tools for organizations seeking to understand, engage, and retain customers in increasingly competitive markets. However, the effectiveness of CRM systems depends fundamentally on the quality of...
The swift advancement of Artificial Intelligence (AI) has necessitated the generation of novel opportunities and efficiency across several sectors. Simultaneously, the dependence on AI for personal data throughout its training process encompasses several...
The relationship between artificial intelligence and data quality is fundamentally bidirectional: AI systems depend on high-quality data for reliable performance, while AI techniques offer powerful tools for assessing, monitoring, and improving data quality...
Personal data privacy is a top priority in the digital age, leading to strict legislation like the General Data privacy Regulation (GDPR) and California Consumer Privacy Act (CCPA). This research study examines the relationship between data privacy,...
Artificial Intelligence (AI) has emerged as a transformative force reshaping governance frameworks across modern organizations. AI-Optimized Full-Stack Governance represents an advanced, integrated governance model that embeds AI-driven intelligence across...
The rapid growth of Large Language Models (LLMs) has transformed multiple sectors, including healthcare, finance, cybersecurity, education, and supply chain management. As AI systems become increasingly integrated into critical decision-making processes,...
Public administration relies on high-quality data for policy development, service delivery, regulatory oversight, and accountability. However, government agencies face persistent data quality challenges that compromise their effectiveness and erode public...
Healthcare is one of the most data-intensive sectors, with patient records, clinical trials, public health surveillance, and research generating vast amounts of information that directly impact patient safety, clinical decision-making, and health policy....
Data quality is commonly understood as a technical challenge requiring measurement, validation, and correction of data errors. However, this perspective overlooks the fundamental socio-organizational dimensions that shape data quality outcomes. This review...
Through a data utilisation value chain, AI/Data commons will promote the construction of an open collaborative ecosystem based on PCI (Participation- Collaboration-Incentives). This will be accomplished by preserving data sovereignty and protecting...
Small and medium enterprises face unique data quality challenges that distinguish them from larger organizations. Limited resources, technical capabilities, and specialized expertise constrain the ability of SMEs to implement comprehensive data quality...
The intersection of artificial intelligence, genetic information, and health research presents complex ethical dilemmas that challenge existing data protection frameworks, including the European General Data Protection Regulation. This article examines the...
The rapid expansion of web-based platforms, digital services, and data-driven applications has significantly increased the complexity of governing online ecosystems. AI-Enabled Policy-Driven Web Governance represents an advanced governance paradigm that...
The swift embrace of cloud computing has revolutionised organisational management of IT resources, requiring strong governance frameworks to tackle the complexities and hazards associated with cloud systems. This paper presents a complete governance...
Research on artificial intelligence in finance often reports model accuracy without explaining how institutional governance shapes real decision outcomes. This review examines the analytical relationship among artificial intelligence integration, data...
The issue of ethical governance for robotics and artificial intelligence (AI) systems is examined in this paper. As a framework to guide ethical governance in robotics and AI, we present a roadmap that connects several components, such as ethics, standards,...
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