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A Conceptual Framework for Real-Time Data Analytics and Decision-Making in Cloud-Optimized Business Intelligence Systems
Subject area: Science,Engineering and Technology · Area of research: Real-Time Data Analytics
Abstract
This paper presents a conceptual framework for integrating real-time data analytics and decision-making within cloud-optimized business intelligence (BI) systems. As businesses increasingly rely on data-driven decision-making, real-time data analytics has become essential for optimizing performance and achieving competitive advantage. The framework proposed in this paper addresses the challenges organizations face when integrating real-time data streams, cloud computing, and advanced decision-making models. It outlines a structured approach that incorporates data acquisition, real-time processing, and decision support, all within a scalable, cloud-based environment. By utilizing cloud-optimized technologies and machine learning algorithms, the framework enables businesses to process vast amounts of data in real time, providing actionable insights that inform immediate decisions. The research draws on existing literature, case studies, and expert opinions, offering a comprehensive synthesis of the current state of real-time analytics and decision-making models. The findings emphasize the importance of aligning technical capabilities with decision-making needs to ensure seamless integration of real-time analytics into organizational processes. The paper concludes by discussing the framework?s potential applications, benefits, and challenges, as well as suggesting future research directions to refine further and expand its use in diverse business contexts.
Keywords
Real-Time Data Analytics, Cloud-Optimized Systems, Decision-Making Support, Business Intelligence, Framework Design, Machine Learning
How to cite this paper
@article{1708317,
author = {Abraham Ayodeji Abayomi, Bright Chibunna Ubanadu, Andrew Ifesinachi Daraojimba, Oluwademilade Aderemi Agboola, Ejielo Ogbuefi; Samuel Owoade},
title = {A Conceptual Framework for Real-Time Data Analytics and Decision-Making in Cloud-Optimized Business Intelligence Systems},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {9},
pages = {713-722},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708317.pdf},
abstract = {This paper presents a conceptual framework for integrating real-time data analytics and decision-making within cloud-optimized business intelligence (BI) systems. As businesses increasingly rely on data-driven decision-making, real-time data analytics has become essential for optimizing performance and achieving competitive advantage. The framework proposed in this paper addresses the challenges organizations face when integrating real-time data streams, cloud computing, and advanced decision-making models. It outlines a structured approach that incorporates data acquisition, real-time processing, and decision support, all within a scalable, cloud-based environment. By utilizing cloud-optimized technologies and machine learning algorithms, the framework enables businesses to process vast amounts of data in real time, providing actionable insights that inform immediate decisions. The research draws on existing literature, case studies, and expert opinions, offering a comprehensive synthesis of the current state of real-time analytics and decision-making models. The findings emphasize the importance of aligning technical capabilities with decision-making needs to ensure seamless integration of real-time analytics into organizational processes. The paper concludes by discussing the framework?s potential applications, benefits, and challenges, as well as suggesting future research directions to refine further and expand its use in diverse business contexts.},
keywords = {Real-Time Data Analytics, Cloud-Optimized Systems, Decision-Making Support, Business Intelligence, Framework Design, Machine Learning},
month = {March},
}