Big Data and Its Applications – Luke Lonergan

Big data is a very large term for data sets so large or complex that traditional data processing applications are inadequate and some of the challenges include analysis, capture, data curation, search, sharing, storage, transfer and visualizations well as information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to take value from data, and seldom to a particular size of data set. Accuracy in big data can lead to more confident decision making process and better decisions can mean greater operational efficiency, cost reduction and reduced risk.

The use and adoption of big data within governmental processes is beneficial and allows efficiencies in terms of cost, productivity as well as innovation and this process does not come without its flaws. Data analysis always requires multiple parts of government both central and local to work in collaboration and create new as well as innovative processes to deliver the desired outcome.

Research on the effective usage of information and communication technologies which is used for development also known as ICT4D suggests that big data technology can make important contributions but also present different challenges to international development. Advancements in big data analysis offer cost saving opportunities to improve decision making process in critical development areas like health care, employment, economic products, crime, security and natural disaster as well as resource management.

Improvements in supply planning and product quality provide the largest benefit of big data for manufacturing and big data provides a structure for transparency in manufacturing industry has the ability to unravel uncertainties like inconsistent component performance and availability.

Big data analytics has helped in improving the healthcare by providing medicine which are personalized and prescriptive analytics, clinical risk intervention and analytics which are predictive, waste as well as care variability reduction, automated external and internal reporting of patient’s data, standardized medical terms and patient registries and fragmented point solutions.

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