Comprehensive Framework for Data Science-Driven Decision-Making Across Healthcare, Finance, Marketing, and Supply Chain Domains
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Data Science is transforming many industries by allowing organizations to use complex and large amounts of data to inform data-driven decision making. In this research paper we explore the methods and tools of Data Science in today's organizational environment, as well as its practical applications. Organizations that implement Data Science through predictive analytics, machine learning algorithms, and large-scale data platforms are able to increase their operational efficiency, make better strategic plans, and improve decision quality. In addition to examining how Data Science is applied in healthcare, marketing, finance, and supply chains, we evaluated the performance of predictive models. Several challenges were identified, including data quality, model interpretability, privacy, algorithmic fairness, and systems integration. Ethical issues and governance frameworks promoting responsible development of AI and analytics are also discussed. Findings confirmed that organizations adopting data-driven approaches achieve significant competitive advantages, improved resource allocation, and support long-term evidence-based decision-making.
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