Owing to the numerous advances in computer and the Internet technology, and now cloud computing services and big data, we are experiencing major changes in our life. Nowadays, the number of factors that influence management is so huge and complex that neither sophisticated theories nor elegant advanced models can offer complete and effective solutions. The challenges are in many ways fundamental that require closer collaboration to draw upon a collective wisdom across different disciplines. Data can enable, encourage and enhance such collaborative linkages. Data Sciences, Information Sciences and Management Sciences are by their very nature interdisciplinary. In our information-intensive era, to remain competitive, maintain constant vigilance and safeguard integrity and security in modern management, we have to rely on big data. Beyond any doubt data analytics has become an integral part of and is integrating information and management sciences. The Journal of Data, Information and Management (JDIM) is a unique and premiere platform for disseminating most up-to-date research and development in the data-information-management interdisciplinary problems. JDIM will offer the business world a venue that prudently balances practical applicability and theoretical rigour.

Key research areas of JDIM include, but are not restricted to:

  • Data Technology and Methodology
    • Sensoring and identification technology
    • Data processing and storage (hiding, fusion, compression, etc.)
    • Data mining
    • Data quality management
    • Granular computing
    • Deep learning
    • Machine learning
    • Neural networks
    • Other technologies and methodologies
  • Applications
    • Data privacy and security
    • Digital marketing and consumer behavior
    • Data-driven production design and development
    • Data-driven quality improvement
    • Data-driven supply chain/logistics optimization
    • Supply chain finance
    • Data-driven intelligent manufacturing
    • Data-driven reliability and maintenance management
    • Intelligent transportation with big data
    • Online decision-making
    • Other applications (Healthcare, government, education, building, etc.)

Journal information

Editors-in-Chief
  • Witold Pedrycz,
  • Xiande Zhao,
  • Xiang Li
Publishing model
Hybrid (Transformative Journal). Learn about publishing Open Access with us

Journal metrics

35 days
Submission to first decision
131 days
Submission to acceptance
39,557 (2020)
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About this journal

Electronic ISSN
2524-6364
Print ISSN
2524-6356
Abstracted and indexed in
  1. ACM Digital Library
  2. CNKI
  3. Dimensions
  4. EBSCO Discovery Service
  5. Google Scholar
  6. Institute of Scientific and Technical Information of China
  7. Japanese Science and Technology Agency (JST)
  8. Naver
  9. OCLC WorldCat Discovery Service
  10. ProQuest-ExLibris Primo
  11. ProQuest-ExLibris Summon
  12. TD Net Discovery Service
  13. WTI Frankfurt eG
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