Authors : Supriya Rajkumar Saw

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/3xuxs7u7

DOI : https://doi.org/10.38124/ijisrt/26Oct892

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Businesses generate vast amounts of data through customer interactions, daily operations, supply chains, and digital platforms. Data science—the integration of statistical methods, computational techniques, and domain knowledge to extract actionable insights from data—has become increasingly important to contemporary business. It helps organizations inform strategic decisions, improve operational efficiency, and strengthen customer relationships. This paper examines the applications and organizational effects of data science, focusing on predictive analytics, machine learning, customer segmentation, and big data management. It considers how these methods are used in marketing, supply chain management, finance, and customer service, alongside the tools and technologies that support their adoption. The review also addresses key benefits and challenges, including data quality, shortages of skilled professionals, privacy concerns, and infrastructure costs. Finally, it considers emerging developments in artificial intelligence, ethical data use, and the integration of data science into organizational practices. It argues that realizing the value of data science requires more than advanced algorithms: organizations must also align their people, processes, and expectations with their analytical goals.

Keywords : Data Science, Business Analytics, Predictive Analytics, Machine Learning, Customer Segmentation, Operational Efficiency, Data-Driven Decision Making.

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