Journal of Data and Information Quality ACM Impact Factor

Journal of Data and Information Quality - ACM | 2024 Impact Factor: 2.9 | Cite Score:5.9 | Q2

Journal of Data and Information Quality With Cite Score

Cite Score and Journal Rank of Journal of Data and Information Quality

About: The Journal of Data and Information Quality is a peer-reviewed journal focusing on the management, quality, and usability of data and information in various domains. It covers a wide range of topics including data quality assessment, data cleaning and integration, information retrieval, data governance, metadata management, information assurance, and the impact of data quality on decision-making processes.
Objective:
The primary objective of the journal is to provide a platform for researchers, practitioners, and academics to publish high-quality research papers, reviews, case studies, and technical notes that contribute to advancing the theory and practice of data and information quality. It aims to foster collaboration and knowledge exchange among experts from academia, industry, and governmental organizations.
Interdisciplinary Approach:
The Journal of Data and Information Quality adopts an interdisciplinary approach, integrating insights and contributions from fields such as information science, computer science, statistics, management science, and economics. This collaborative perspective enables comprehensive exploration and innovation in methodologies, tools, and best practices for ensuring data reliability, accuracy, and usability.
Impact:
The journal significantly impacts both academia and industry by influencing the development of standards, frameworks, and technologies for improving data quality and information management practices. Its publications contribute to advancements in data governance strategies, data integration techniques, and methodologies for assessing and enhancing data quality across various domains and applications.
Significance:
The Journal of Data and Information Quality is significant in advancing the field of data and information quality by disseminating authoritative research findings and practical insights. By publishing high-quality, peer-reviewed articles, the journal supports continuous improvement and innovation in data management practices, ultimately contributing to enhanced decision-making processes, organizational efficiency, and data-driven innovations in both public and private sectors.

Journal Home: Journal Homepage

Editor-in-Chief: Felix Naumann

Scope: The Journal of Data and Information Quality (JDIQ) focuses on research related to the quality aspects of data and information across various domains. Here are the key areas typically covered in this journal:
1. Data Quality Assessment and Measurement
Methods and frameworks for assessing data quality
Metrics and criteria for measuring data quality
Case studies and evaluations of data quality in different contexts
2. Data Cleaning and Integration
Techniques and algorithms for data cleaning and preprocessing
Data integration and data fusion methods
Handling inconsistencies and redundancies in datasets
3. Information Quality Management
Strategies and practices for managing information quality
Information governance and data stewardship
Quality assurance frameworks for information systems
4. Data Profiling and Discovery
Profiling techniques to understand data characteristics
Data discovery and exploration methods
Tools and platforms for data profiling and metadata management
5. Trust, Privacy, and Security of Data
Trustworthiness and reliability of data sources
Privacy-preserving techniques in data management
Data security and protection measures
6. Quality of Big Data
Quality issues specific to big data environments
Scalability and performance considerations in big data quality
Case studies and applications of big data quality management
7. Quality Issues in Information Systems
Quality of information systems and databases
Impact of data and information quality on decision-making
Real-world applications and case studies on information system quality
8. Data Governance and Policy
Policies and regulations influencing data quality practices
Standards and best practices in data governance
Compliance and ethical considerations in data management
9. Quality Improvement and Assurance
Strategies and methodologies for continuous improvement of data quality
Quality assurance processes and frameworks
Quality control mechanisms in data-driven organizations
10. Emerging Trends and Future Directions
Exploration of emerging trends in data and information quality
Innovations and technologies shaping the future of data quality
Challenges and opportunities in advancing data and information quality research