Data Science
Big data analytics and e-healthcare
As the term implies, big data analytics is concerned about the analysis of a diverse range of data or ‘big data’ to get access to varied forms of information. The information gained from examining the big data present in the company’s repository helps in formulation of informed business decisions and implementation of newer and better policies aimed to enhance the production of the business. Such information may include some hidden patterns or decryption keys for certain ciphertexts, recent trends observed in the business market, or distinguished customer preferences.
Various trends have been observed to be spreading across the market in different sectors including the e-commerce and e-healthcare facilities in scale as well as application patterns of the big-data analysis. Big data analytics with the help of parallel and distributed systems successfully manages the huge repository of data that increase exponentially in size with time. Besides, it has been implied that characteristics of hardware and the software makeups of the system impacts the big data analysis (Kambatla et. al., 2014).
Data Analysis: Benefits and drawbacks
Data analysis is a method of obtaining useful information from diverse sources and help making informed decision based on the data. The three distinct processes involved in the system of data analysis are- cleaning the obtained data, transforming or decoding the data, and finally to model them.
Researchers put forward certain means of data analysis including the method of functional data analysis. Other significant descriptions like use of derivatives in the functional modeling and estimation in variation of phases are also described (Ramsay, 2004).
Benefits
Sometimes it is cumbersome to accumulate a vast range of data regarding the customers’ or patients’ physical and clinical data and to analyze them individually. Hence, the technique of big data in healthcare has been introduced which helps in accumulating, analyzing, and leveraging customer’s data in an order. The big data program has significantly boosted the rise of e-healthcare facilities that are available today.
Numerous applications have been launched in the field of e-healthcare and EHR data is one of the applications most commonly used. These big data contain information for precision medicine and it provides significant amount of knowledge about the quality of healthcare provided (Wu et. al., 2016).
Challenges
As evident from the above discussion that the e-healthcare facilities are enhanced by the help of data analysis techniques which successfully helps in keeping a track of the vast information important for proper diagnosis; but the data analysis also poses several challenges. The vast amount of data also welcomes several risk factors that confuse the risk management teams.
In order to eradicate such threats, the analysts aim to formulate numerous strategies but oftentimes left with interlocking data sets. Therefore, a need arise for such a data system that automatically collects, organizes, and analyses the information collected from diverse sources.
References
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561-2573.
Ramsay, J. O. (2004). Functional data analysis. Encyclopedia of Statistical Sciences, 4.
Wu, P. Y., Cheng, C. W., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016). –Omic and electronic health record big data analytics for precision medicine. IEEE Transactions on Biomedical Engineering, 64(2), 263-273.