International Journal of Business Intelligence and Big Data Analytics
Advancing Research in Business Intelligence and Big Data Analytics for Informed Decision-Making
Peer Review Policy
The International Journal of Business Intelligence and Big Data Analytics employs a rigorous double-blind peer review process, ensuring anonymity for both authors and reviewers to provide an unbiased assessment of all submissions.
| Reviewer Criteria | Evaluation Aspects |
|---|---|
| Expertise in Business Intelligence | Relevance, Clarity, Innovation |
| Experience in Big Data Analytics | Significance, Methodology, Practical Implications |
Each manuscript is reviewed by at least two experts selected based on their field expertise, research history, and publication track record. Reviewers evaluate for originality, significance, and methodological robustness, offering constructive feedback to authors.
Editorial Oversight
The Editor-in-Chief and Associate Editors oversee the peer review process, ensuring constructive feedback and making publication decisions based on reviewer recommendations.
Ethical Standards
- • Reviewer Ethics: Reviewers must disclose conflicts of interest, maintain confidentiality, and provide unbiased and constructive feedback.
- • Author Responsibility: Authors are expected to submit accurate data, acknowledge prior research, and adhere to ethical standards in business intelligence and big data research.
Interested in Submitting Your Research?
Contribute to the International Journal of Business Intelligence and Big Data Analytics and help advance knowledge in data-driven decision-making.
Submit Your ManuscriptCurrent Issue
The International Journal of Business Intelligence and Big Data Analytics (IJBBDA) is a peer-reviewed academic journal that focuses on cutting-edge research and innovative solutions in the areas of business intelligence and big data analytics. In this 2023 issue, the journal presents a collection of high-quality articles and case studies that explore various topics related to these fields.
The issue covers a range of topics including:
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Data-driven decision-making: The use of big data analytics to make informed business decisions.
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Machine learning and artificial intelligence: The application of AI and ML in business intelligence and data analytics.
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Predictive analytics: The use of statistical and machine learning techniques to predict future outcomes based on historical data.
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Data visualization and storytelling: The art of presenting complex data in a visually appealing and engaging manner.
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Big data architecture and infrastructure: The design and implementation of scalable and reliable big data systems.
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Business process optimization: The use of big data analytics to optimize business processes and improve operational efficiency.
The articles in this issue present a wealth of knowledge and practical insights that can help organizations make better use of their data assets. They showcase the latest advancements in the field of big data analytics and provide a comprehensive understanding of the challenges and opportunities that come with managing and analyzing large volumes of data.
