Big Data: Gold or Hype?

1 Introduction

According to Singh, Garg, and Mishra (2015), they define that big data is that size of data that cannot be kept and managed by a solo engine (Singh, et al., 2015) while other author said that Big data is an mass collections of different technologies subsequent in data processing competences that have been unknown before (Blasiak, 2014). Big Data also may lead to positive and negative effect towards all industries in global, consumers and governments (Poremba, 2013)

According to business perspective, businesses that invest in big data analysis and positively grow value from their data will have a different advantage over their competitors while as a governance to them businesses do not need to protect all the data, but they need to start sharing data with in-built protections with the right levels and functions of the organization based on government policies (EYGM Limited, 2014). Big Data analytics are as social progression which diverse stakeholders interact to generate, use and govern analytical methods determined by big data (Someh, et al., 2016).

Based on Statista(2018), figure 1 shows the market size of big data from 7.6 billion U.S Dollar in revenue on 2011 are increasing constantly on their market size to 34 billion U.S Dollar on 2017 in the global (Statista, 2018). The sectors that use the biggest data are healthcare, retail, music and entertainment, telecommunication, manufacturing, public sector, finance and insurance, energy and transportations (Zillner, et al., 2016). 

2 Big Data- A Gold Mine or a Hype?

2.1 Managerial Perspective

Big data have been giving a positive impact towards managerial in accurately in decision making of the businesses (Bhadani & Jothimani, 2016). By having big data in order to make decision making are summarizing big data as decision provision, data benefits mostly to get a better understanding of the present condition and lets to analyse situations. Thus, big data empowers decision creators to make try-outs with their result without having to transmit out the decision (Blasiak, 2014; Frizzo-Barker, et al., 2016). Managers have been offered from the big data analytics with the ability to measure about their businesses, market, customers and so on and directly interpret the data into greater decision making and performance (Frankel & Reid, 2008).

Amazon shows that they are in specific very well in evolving fresh business models with the assistance of big data and rise the efficiency of their operations significantly (Anderson & Zettelmeyer, 2017). The advantages are obvious, better analytics allow companies to comprehend their business atmosphere better and make more decisions that will lead to an improved position in the marketplace (Balachandran & Prasad, 2017).

Although big data are one of a big phenomenon happen for all stakeholders but as a company without having any skilled worker to handle big data analysis it won’t be success after all. Leaders in both the private sector and public sector agree that Data science and analytics (DSA) will change the face of business and government, but skills lacks continue to postponement its implementation and operation (Gamage, 2016). According to figure 2, the table shows DSA skills are in high demand, but supply is critically low with companies facing undecorated shortages (IDC, 2015). 

For example, Healthcare is one of a vital division with growing demand for DSA-skilled workforces. By 2022, the global eHealth market, power-driven by DSA, is predictable to outstrip US$300 billion (ICTC, 2016).

2.2 Technological Perspective

There are possibly more than one big data technologies that have enlighten the benefits on big data towards all stakeholders but mostly importantly is for businesses and government to track all the big data technologies such Hadoop (Padhy, 2013) and MapReduce Framework (Dean & Ghemawat, 2004).Technologies itself can help to provide elements of ethical mix where it can be take form of intelligent data use trackers that tell how data are used and making humans to make decision to use the data or not which its under our control and awareness (King & Richards, 2014).

Big Data technologies can be used for storing and processing such as medical records (Schiele & McAlpin, 2014). Streaming data can be apprehended from devices or machines attached to patients, stored in HDFS and investigated speedily (Kerzner & Maniyam, 2013). With Big Data tools and human genome mapping, there may be a usual for people to have their genes mapped as the part of their medical record. Genetic factors that cause an illness will be easier to find, which help in the advance of modified medicine (Data Science Series, 2012).For example, Cleveland Clinic used big data technologies to collect and organizes all their patients’ details such as patients’ risk factors and evaluation of the success treatments (Rubenfire, 2015). Figure 3 shows the patient centric health care ecosystem from big data perspective (Palanisamy & Thirunavukarasu, 2017). 

2.3 Ethical