Data Science and Big Data Analysis in Data Mining Discussion Data mining is the process that is used for the discovery of new and essential data, conclusions with meanings, and helping in decision making after the data has been modeled, cleansed, transformed, and inspected. Data analysis is used to make accurate decisions and helps organizations run more effectively.
An analysis is a process involving the splitting of a sample under study into different components of its own and examining the gathering information or results that are later used for decision making (Wickham, 2016). Various sectors, including healthcare sectors, are practicing the use of big data analytics. The analytics do not always bring with it benefits but also has challenges in its application.
Big data analytics help improve patient care by making it simple to acquire health records electronically, helping collect medical information, including clinical data, medical conditions, lab tests and results, and diagnoses (Bazeley & Jackson, 2013). Simplicity in obtaining all these data helps healthcare workers give quality care.
Healthcare sectors also use big data analytics to advance their efficiency in operating activities like acquiring the rates of admission of patients and state the ability of the staff.
Big data analytics help to discover a cure for diseases. In cases where a specific medication is useful in some patients and is ineffective in others, big data is used to detect hidden patterns and examines large groups of data to give insights.
It is not always simple to capture accurate, correct formatting, clean, and complete to use in big data analytics.
Healthcare sectors mostly have a hard time cleansing their data, also known as scrubbing. Data cleansing makes sure that there is accuracy, correctness, consistency, and relevancy in datasets and also makes sure that the data set is not corrupted.
Healthcare sectors also tend to be vulnerable when it comes to data security. Such vulnerabilities include; phishing attacks, hackers, and fire breakouts; these make it possible to secure data, especially if no effort is injected to deal with the vulnerabilities.
Bazeley, P., & Jackson, K. (Eds.). (2013). Qualitative data analysis with NVivo. SAGE publications limited.
Wickham, H. (2016). ggplot2: elegant graphics for data an
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