This intellectual inquiry explores the profound influence of data quality on data analytics. The immaterial commences by delineating the multifarious aspects of data quality, which include accuracy, completeness, consistency, reliability, and timeliness. It...
The quality of data, particularly its assessment, has been extensively examined in both research and practical applications. To facilitate economically driven management of data quality and decision-making under uncertainty, it is crucial to evaluate the...
Data health pertains to the comprehensive quality, usefulness, and value of data inside an organization. Inadequately handled data can obstruct decision-making, squander resources, and result in lost opportunities. Data profiling is the essential initial...
The primary objective of this thesis is to create a data quality scorecard (DQS) that connects the data quality requirements of the Data Warehouse at Brewage Ltd Group with certain data quality dimensions. In the initial iteration, utilizing Design Science...
Data warehousing has undergone significant transformations since its inception, yet several critical areas remain unexplored. Traditional models, such as the Inmon and Kimball approaches, laid the foundation for structured data management. However, emerging...
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...
Data governance has become a pivotal element in assessing the efficacy and enduring stability of IT systems in the contemporary data-centric landscape. It includes frameworks that assist organizations in overseeing data collection, storage, processing,...
In the era of digital transformation, organizations are increasingly reliant on cloud-centric data engineering frameworks to manage vast amounts of data efficiently. The exponential growth of data, coupled with its critical role in driving business...
Large amount of data available for the organization for which by which the same can influence its business decision. As large amount of data has been collected from distinguish resources and as the data has not been filtered which ultimately affect the...
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...
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...
Data quality has emerged as a fundamental pillar of scientific integrity, organizational performance, and evidence-based decision-making in the contemporary digital landscape. As artificial intelligence systems become increasingly integrated into...
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...
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....
In the contemporary digital economy, organizations increasingly depend on data as a strategic asset for decision-making, innovation, and regulatory compliance. As a result, data governance has emerged as a critical organizational function that ensures data...
Data quality assurance is a critical but resource-intensive component of citizen science projects. As project scale and data volume have grown, traditional expert-based quality assurance approaches have become increasingly inadequate, creating a need for...
The exponential growth of data generated by digital platforms, enterprise systems, and emerging technologies has significantly increased the complexity of data governance within modern organizations. Data Governance Analysts are tasked with ensuring that...
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...
Digital transformation has restructured company processes, enhancing efficiency, innovation, and competitiveness using artificial intelligence (AI) and sophisticated analytics. The fast implementation of AI-driven processes presents considerable regulatory...
Data mining has become a fundamental component of knowledge discovery across diverse domains such as healthcare, finance, scientific research, and business intelligence. As data volumes, sources, and analytical techniques continue to expand, the need for...
The FAIR principles Findable, Accessible, Interoperable, and Reusable—have emerged as a foundational framework for scientific data management, providing guidance for ensuring that data remain valuable and usable over time. This review examines the...
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,...
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...
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...
Data validation is a critical yet resource-intensive component of citizen science projects. As the volume and complexity of citizen science data continue to grow, traditional approaches to quality assurance—relying on expert review or simple filtering rules...
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