A Review on Big Data Analytics and its Application in Medical Field

To describe the promise and potential of big data analytics in healthcare and demand forcasting. The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Data from the health system, everyday data and genomic data have the potential to be used in predictive analytics models that help to improve forecasting of demand for health care services. A supportive infrastructure should be developed that allows interoperable multi - source data sets to be used in predictive analysis in a way that ensures individuals? data privacy requirements are met. Once this has been achieved, there may be the opportunity to use the insights from predictive models to inform network - level health system strategies. Big Data refers to the data or sets of records that are too large in volume to be operated using the existing database management tools and techniques. They have many important applications, such as search engines, business informatics, social networks, social media, genomics, medical field, meteorology, and weather forecast. The growing need to customer behavior analysis and demand forecasting is deriven by globalization and increasing market competitions as well as the surge in supply chain digitization practices. In this study, we performed a thorough review for applications of predictive big data analytics (BDA) in SC demand forecasting. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome. The main objective of this paper is to give a brief introduction of Big Data, its application and analysis in future and challenges.