<![CDATA[Big Data Analytics (BDA) has become a pivotal enabler of data-driven decision-making across various industrial sectors. This narrative review aims to synthesize existing literature on BDA techniques, tools, and applications to identify their role and impact in decision support systems. The review draws upon scholarly databases such as Scopus, IEEE Xplore, and Google Scholar, utilizing a systematic search strategy with Boolean keyword combinations to retrieve relevant literature. Studies were screened based on inclusion and exclusion criteria, focusing on empirical findings and practical applications of BDA across domains. Findings reveal that techniques such as data mining, predictive analytics, and machine learning offer enhanced accuracy and real-time capabilities, leading to better outcomes in healthcare diagnostics, manufacturing efficiency, and logistics optimization. The utilization of platforms like Hadoop, Spark, and Tableau demonstrates both functional versatility and implementation challenges, influenced by cost, infrastructure, and human capital readiness. Furthermore, the success of BDA initiatives is closely linked to organizational factors including data quality and workforce expertise. Systemic barriers such as strict data policies, fragmented IT infrastructures, and limited data access in low-resource settings impede optimal BDA deployment. This review underscores the need for strategic policy reforms, technological investments, and capacity building to realize the full potential of BDA. By addressing existing limitations and supporting future research directions, organizations can harness BDA to enable informed, agile, and sustainable decision-making.]]>
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