The role of data science in transforming business operations: Case studies from enterprises

This review and methodology looks at how data‑science methods are reshaping modern business operations. It draws on case studies from four areas—marketing, supply‑chain management, finance, and customer service—to evaluate three key techniques: – Predictive analytics – Sentiment analysis – Mathematical optimization The study examines how machine‑learning models and emerging technologies such as the Internet of Things (IoT) and blockchain are being integrated to boost efficiency and improve strategic decisions. A central focus is modeling the move from old, isolated data silos to strong, decentralized data architectures that enable real‑time analytics. The framework also tackles three major challenges that enterprises face: – Ethical data governance – Algorithmic bias – Infrastructure scalability

It offers practical guidance for building simulation models that measure both tangible benefits—like reduced operational costs—and intangible outcomes—such as better customer experience and higher productivity. In short, the work provides a systematic method for evaluating data‑driven operational approaches, giving a solid foundation for academic review and for designing enterprise systems.

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