Big Data Analytics for Healthcare Organisation

Running head: BIG DATA ANALYTICS FOR HEALTHCARE ORGANISATION
Big Data Analytics for Healthcare Organisation
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1BIG DATA ANALYTICS FOR HEALTHCARE ORGANISATION
Introduction
In healthcare industry the emerging technologies are not properly being grasped to
increase the potential benefit. “Big data analytics: Understanding its capabilities and potential
benefits for healthcare organizations” authored by YichuanWang, LeeAnn Kung and Terry
Anthony Byrd in an article published in 2015 that examined the historical development,
architectural design and functionalities of the components in Big Data Analytics. The study
also recommended five strategies for the healthcare industry to adopt Big Data Analytics to
increase their capabilities in data-driven operations (Wang, Kung and Byrd 2015).
The focus of this Essay is to understand the dynamic potentialities of using Big Data
Analytics in healthcare industry highlighted by the chosen literature. This essay will also
present the implications highlighted by other researcher to critically explore the potential
benefits of using Big Data in Healthcare service. However, as per the chosen article and other
literatures Big Data Analytics can allow the healthcare service provides huge number of
opportunities to take more data-driven and evidence based decision while executing a more
controlled and efficient social service.
Big Data Analytics
The term Big Data refers to the huge volumes of data that cannot be stored, processed
and fetch properly in conventional way of database application usage. In order to manage this
huge amount of data a complex data analysis algorithm is used and this process is known as
Big Data Analytics (Wang, Kung and Byrd 2015). The Big Data technology has passed its
evolutionary stage between 2001 and 2008. Several other software application developments
such as Extendable Markup Language (XML), Database Management System (DBMS), Web
development ad Hadoop enhanced the complexities of the core big data analytics model with
their diverse data set (Wang, Kung and Byrd 2015). The purpose of these advancements is to

2BIG DATA ANALYTICS FOR HEALTHCARE ORGANISATION
increase the opportunity of the end users to store, fetch and process an indescribably huge
amount of data in a real-time platform. The process of Big Data Analytics is framed by the
Big Data Architecture that holds five major layers of data handling namely data, data
aggregation, analytics, information exploration, and data governance. As supported by Wang,
Kung and Byrd (2015), the cloud based big data operation also enhanced the opportunity
engage third party based distributive operational handling through cost effective storage
system, which is known as “big data in the cloud”. In a nutshell the Big Data Analytics
system is able to deliver the user a appropriate set of data through analysing a huge volume of
different data types, that can enable the user to take more effective decision and perform
more data-driven operations (Wang, Kung and Byrd 2015).
Utilization of Big Data in healthcare organisations
Healthcare industry needs tons of dataset comes from different data source such as
patient details, medical resource details, treatment plans, human resource details, regulatory
compliance and many others. According to Wang, Kung and Byrd (2015), in 2011 healthcare
industry around the world stored healthcare data of 150exabytes that is 15x109GB, through
electronic health record system. It clearly indicates that utilising this huge number of data for
data driven statistical analysis to predict a specific outcome of treatment plan or health
condition will be highly beneficial. As stated by Wang, Kung and Byrd (2015), the huge
amount of organisational data stored from the healthcare industry worldwide is highly
utilisable for economical analysis of healthcare interventions. Big Data Analytics can be also
utilised as a major attribute of Evidence Based Practice in healthcare environment.
As defined by the chosen article, the Big Data architecture of a healthcare system
should include four major components namely Data governance, Data management and Data
lyfe-cycle and Data privacy-security (Wang, Kung and Byrd 2015). Data governance will be
used to operating different data layers in healthcare environment that includes collecting data