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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness

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  • Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness

Olalekan Hamed Olayinka *

Statistics, Analytics and Computer Systems, Texas A & M University, USA.

Review Article
 
International Journal of Science and Research Archive, 2021, 04(01), 280-296.
Article DOI: 10.30574/ijsra.2021.4.1.0179
DOI url: https://doi.org/10.30574/ijsra.2021.4.1.0179

Received on 13 October 2021; revised on 23 November 2021; accepted on 25 November 2021

In an increasingly interconnected and data-saturated business environment, organizations are challenged to extract actionable insights from vast and heterogeneous data sources. Big Data integration, when effectively harnessed, offers a transformative pathway for improving operational efficiency and enhancing market responsiveness. The convergence of structured and unstructured data—ranging from transactional records and sensor feeds to social media and customer interactions—requires advanced platforms capable of high-velocity processing and seamless interoperability. Traditional batch-oriented analytical models are being eclipsed by real-time analytics, which enable businesses to monitor, assess, and respond to events as they unfold, significantly reducing latency in decision-making. Real-time analytics embedded within enterprise systems facilitate dynamic process optimization, predictive maintenance, demand forecasting, and rapid customer service interventions. Industries such as manufacturing, logistics, finance, and retail have increasingly turned to real-time data ecosystems to streamline workflows, reduce costs, and preempt operational disruptions. Moreover, by integrating big data with cloud computing, IoT devices, and AI-driven algorithms, businesses can adapt more swiftly to changing consumer behavior, supply chain shifts, and competitive pressures. However, realizing the full potential of big data and real-time analytics entails overcoming significant challenges—including data silos, infrastructure complexity, data privacy, and skill gaps. This paper explores the strategic architecture of big data integration, real-time analytics platforms, and the technologies enabling end-to-end visibility and agility. It further examines case studies where real-time data pipelines have driven measurable gains in performance, offering a roadmap for organizations aspiring to become more intelligent, adaptive, and competitive in fast-moving markets. 

Big Data Integration; Real-Time Analytics; Operational Efficiency; Market Responsiveness; Predictive Insights; Data Architecture

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2021-0179.pdf

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Olalekan Hamed Olayinka. Big data integration and real-time analytics for enhancing operational efficiency and market responsiveness. International Journal of Science and Research Archive, 2021, 04(01), 280-296. Article DOI: https://doi.org/10.30574/ijsra.2021.4.1.0179

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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