Big Data Term – Interpretation

Although the term Big Data has gained in considerable popularity in recent years, there is no consensus how to interpret it. According to Devey (2014), Big Data refers to a technological phenomenon which rose in the mid-1980s due to the enhancement of computers, and growing storage and processing capacities. These have offered new and improved ways of sifting through the infinite quantities of data available. Pence (2014) described Big Data not only by the amount of information but also by its variety and complexity as well as the speed at which the data have to be analyzed or delivered. The term Big Data is frequently understood as databases which, due to their massive size, are difficult to manage using conventional software (Manyika et ah, 2011). According to Hassan (2014), Big Data are data sets whose rate of growth is exponentially high over a time interval. Typical data mining tools do not serve their analysis. Such data sets result from daily capture of stock exchange, any credit card user’s timely usage trends, insurance cross line capture, health care services, etc. In real time, these data sets go on increasing and create complex scenarios over time.

Cox and Ellsworth (1997) while discussing the concept of Big Data pointed out to two different meanings of this concept: Big Data collections and Big Data objects. However, they argued that for processing such huge collections and objects it becomes necessary to use nonstandard algorithms and software. SaaS defines Big Data as a popular term used to describe the quick growth and availability of data, structured, unstructured, and semi-structured. Structured data consist of sets with a defined structure like databases. They comprise data tables with defined columns and rows intertwined (database relationships). Structured data come from records of people’s or other objects’ behavior (transactions, clicks, likes, logins, etc.) and from sensors/detectors referred to as part of the IoT. Unstructured data do not have specified structure. These are data generated by people in the form of spoken language, video files, or images. Semi-structured data are similar to structured data but can be extended with some unstructured data. Data stored in XML, RSS, or JSON format constitute this category.

Many authors associate Big Data with the Internet and social media as a new social phenomenon (Hsu, Chang, & Hsu, 2017). According to them, Big Data are a new generation of technologies and architectures which are designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and analysis (Halaweh & Massry, 2015). It is said that Big Data are a technological innovation where complex unstructured and structured data are parallelly distributed, stored and direct queries could be applied to those data stored (Puschamann, Burges, 2014). Through Big Data services, organizations could better monitor the acceptance of products in the marketplace and understand its business environment, potentially fueling competitive advantages. Lu, Li and Zhang (2015) posit that Big Data analytics applications are a new type of software applications that process large-scale data utilizing large parallel processing framework to obtain hidden value.

Schamrzo (2013) points out that Big Data are not about technology but about business transformation—transforming organizations from retrospective, batch, data-constrained structures that monitor the business environment into predictive, real-time, data hungry structures that optimize the business environment. Chen &t Zhang (2014) classified Big Data as one of five “critical technical areas” contributing to Business Intelligence and analytics, the others being text analytics, web analytics, network analytics, and mobile analytics. They view Big Data as a subset of Business Intelligence. They stated that Business Intelligence could be seen as an umbrella term that includes applications and tools that enable access and analysis of Big Data and information in order to improve business performance, competitive advantage, and create business value. Many authors argue that Big Data mean new ways of running a business, of decision-making, and of understanding the customers, suppliers, and other stakeholders of organizations (McAfee Sc Brynjolfsson, 2012; Schmarzo, 2013). It is stated that Big Data enables the creation of innovative business models, products, and services. It gives organizations a way to outperform the competition. This kind of data may be used to achieve a better understanding of an organization’s customers, employees, partners, and operations.