Antonia Pauca
Student No: 1804860
Use of Big Data is a positive step forward for humankind
Introduction
Since its practical use, Big Data has impacted the way we manage, analyse and influence data in any industry. Among these, one in which it plays a pivotal role, and shows great promise, is healthcare. Through its use, healthcare analytics has brought numerous benefits, and have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve the quality of life in general. However, along with the increase of the average human lifespan and overall world population in general, new challenges present themselves to today’s health and treatment delivery methods. To tackle these challenges, health professionals, much like business entrepreneurs, collect and analyse massive amounts of information, with a scope of discovering newer and more practical solutions.
However, the big question comes – is it all a positive step forward, or is there potential for some negative impacts as well?
Context
Before we answer that question, we first need to explore the definition of Big Data. This has been defined by many in various ways, one of them being that Big Data is a large volume of information – both structured and unstructured – which overwhelms a business on a day to day basis. Some of the data sets may be analysed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions (Kaminskiy, 2017).
As previously mentioned, applying Big Data analytics in healthcare brings many positive and life-saving outcomes. It brings forth enormous and vast quantities of information, created by the digitization of everything that, through the use of technology, gets consolidated and analysed. Analysing and applying specific health data of an individual or any number of people enables us to abolish and prevent epidemics, cure diseases, reduce costs of medicine, and more (Raghupathi, 2014).
Originally, Big Data has been broken down to three dimensions by IBM data scientists, known as “The 3 V’s of Big Data”: volume, variety and velocity. However, more recently we can find it as being defined by the following ten properties: volume, veracity, velocity, variety, value, validity, variability, volatility, vulnerability, visualisation and value.
The name 'Big Data' itself relates to size, which is of enormous proportions. In terms of data analytics, size plays a crucial role in determining the value of data. Furthermore, whether a particular data can actually be considered as a Big Data or not, is dependent upon its volume. Hence, 'volume' is one characteristic, which needs to be considered while dealing with 'Big Data' (Jenn Cano, 2014).
Volume refers to the incredible amounts of data generated each second from social media, cell phones, cars, credit cards, M2M sensors, photographs, video, and so on. The vast amounts of data have become so large in fact that we can no lon...
