BIG DATA ANALYTICS IN SCIENTIFIC RESEARCH

Abstract

With the amount of data increasing on a daily basis, there has been a provision of platforms to help in management, analysis of this data and therefore turning this raw data into something meaningful and informed. Therefore, embracing high performance computing extends the boundaries of research by allowing breakthroughs in healthcare, environmental health, genetics, engineering, astronomy, anthropology, and other subjects. Big Data Analytics is created in the aim of providing decision making based on evidence. This means using past trends to identify behaviors of the future is paramount in influencing the outcome of a research project. The developments of computer technology such as machine learning, artificial intelligence, the explosion in the use of cloud computing and data mining, and other related advances have made researchers even more capable of deriving useful and much welcome information from large datasets. Despite the significant benefits, the deployment of the system is hindered by the drawbacks of ethical issues, data privacy and confidentiality, data scaling, lack of quality data and integrity, and the cost to design, build and maintain the infrastructure. In this work, the issues of data analytics specifically Big Data in the context of modern scientific method are discussed, and the technologies, areas of their application, as well as the advantages, drawbacks, and prospects of the assimilation come into focus. The study argues that Big Data technologies encourage the efficiency of scientific research by enhancing innovativeness, expediting research processes, and promoting cooperation of researchers of various fields, thereby enhancing scientific and sustainable appreciation.