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Huge amount, high speed, versatility converts traditional database into Big Data. These characteristics make big data more difficult and challenging for compilation. The techniques used to compile traditional data; it cannot be used to compile such big data as it is not always collection of structured data (Undavia 2018). Such informative mass of data prove crucial for purpose of analytics.
This is because of availability of sophisticated data storage, which stores this huge amount of data. You cannot imagine a world without such data store where versatile information like details of person, organization, transaction performed, etc. are stored. This data can be used to extract valuable information and knowledge for the growth of the particular organization or improved activity. In the recent time, all aspects of information is available related to a customer. It ranges from customer name, its details about the purchase, his social connection, professional association, etc., This proves that, in current time data is the founding stone and vital element of any organization (Elragal 2014).
The way Big Data is defined is changed against its aspect of application. In the recent time, it is characterized as a collection of data elements whose size, speed, type and complexity is changed frequently (Belle 2015). These frequent changes makes one to seek, adopt and invent new hardware and software mechanisms to manage, store, visualize and analyse these form of data.
The three Vs are Velocity, variety and volume, playing very important role in all various applications of such data. Healthcare is one of the prime user of this Big Data technology. In health care, data is widely spread among multiple health care systems, health insurers, researchers, government entities etc (Manyika 2011). The data repositories are not always sophisticated enough which can provide platform of transparency for global data. As these kind of data have huge size, versatility in variety and rapid changing in nature, new type of data analytics, well-structured storage and accurate analysis methods are required. This huge amount of data then can be analysed properly and desired information can extracted from them.
Application of the Big Data technology in healthcare has large number of positive outcomes and such outcomes have life-saving phenomenon. Like other fields, in healthcare also, vast quantities of data and information is created/generated through digitization, automatic sensor data, etc. and then these mass have to consolidated and analysed by some specific technologies (Rehermann n.d.).
The technological advancements have also inferred the way healthcare systems work. Treatment models, data capturing of population or a person, deciding the treatment once a disease is diagnosed, the model of diagnosis, etc. have changed drastically with the advent use of the recent technologies. This all changes are taking place based on the data collected of the same domain so that proper corrective and improved steps can incorporated in recent and newer technologies. Most of these changes are data driven so the healthcare domain also become conscious about the data collection and storage. For the physician who are treating their patients, they always eager to know as much as possible about his patient. This early stage of detection will help the doctors to decide mode of the treatment especially in case of some serious illness or disease. It is already proved that curing of such critical illnesses in early stage is more effective and less expensive as well.
When healthcare industry attempt to use the big data comprehensively, micro level analysis can be done for patient as huge amount of versatile data will be made available about the patient’s history, way of treatment of other patients of same decease, other related parameters to cure this decease.
A tailored package of the data can be provided to doctors for better life-saving outcomes.