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Innovations in Data Modeling and Transformation for Scalable Business Intelligence on Modern Cloud Platforms
Subject area: Science,Engineering and Technology · Area of research: Cloud-based Business Intelligence (BI)
Abstract
This paper explores innovations in data modeling and transformation frameworks that enable scalable Business Intelligence (BI) on modern cloud platforms. As organizations increasingly rely on cloud environments to process and analyze large datasets, the limitations of traditional on-premises BI systems have become evident. The paper examines key advances in cloud-based BI, including semantic and logical data modeling, decentralized approaches such as data mesh and domain-oriented design, and real-time streaming data models. Additionally, the role of modern ELT pipelines, serverless computing, and DataOps in automating and optimizing data transformation workflows is discussed. The paper also highlights the integration of artificial intelligence (AI) in enhancing data quality through anomaly detection and auto-mapping. These innovations collectively empower organizations to scale their BI capabilities, streamline data governance, and achieve faster, more agile decision-making. Finally, the paper identifies key directions for future research, including unified metadata layers, AI-augmented BI, and adaptive cloud cost modeling. These advancements are pivotal in shaping the future of BI on cloud platforms, offering unprecedented flexibility, scalability, and operational efficiency.
Keywords
Cloud-based Business Intelligence (BI), Data Modeling, Data Mesh, ELT Pipelines, Serverless Computing, AI-enhanced Data Transformation
How to cite this paper
@article{1708319,
author = {Jeffrey Chidera Ogeawuchi, Abel Chukwuemeke Uzoka, Abraham Ayodeji Abayomi, Oluwademilade Aderemi Agboola, Toluwase Peter Gbenle; Olanrewaju Oluwaseun Ajayi},
title = {Innovations in Data Modeling and Transformation for Scalable Business Intelligence on Modern Cloud Platforms},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {5},
pages = {406-415},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708319.pdf},
abstract = {This paper explores innovations in data modeling and transformation frameworks that enable scalable Business Intelligence (BI) on modern cloud platforms. As organizations increasingly rely on cloud environments to process and analyze large datasets, the limitations of traditional on-premises BI systems have become evident. The paper examines key advances in cloud-based BI, including semantic and logical data modeling, decentralized approaches such as data mesh and domain-oriented design, and real-time streaming data models. Additionally, the role of modern ELT pipelines, serverless computing, and DataOps in automating and optimizing data transformation workflows is discussed. The paper also highlights the integration of artificial intelligence (AI) in enhancing data quality through anomaly detection and auto-mapping. These innovations collectively empower organizations to scale their BI capabilities, streamline data governance, and achieve faster, more agile decision-making. Finally, the paper identifies key directions for future research, including unified metadata layers, AI-augmented BI, and adaptive cloud cost modeling. These advancements are pivotal in shaping the future of BI on cloud platforms, offering unprecedented flexibility, scalability, and operational efficiency.},
keywords = {Cloud-based Business Intelligence (BI), Data Modeling, Data Mesh, ELT Pipelines, Serverless Computing, AI-enhanced Data Transformation},
month = {November},
}