Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decisionmaking. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V’s characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions.

Introduction and motivation

The notion of Big Data Analytics (BDA) is driven by underpinning new waves of innovation, analytic services with intelligence and stirring advances in technology over the last few decades. The emergence applications of BDA have prompted the attention of many academic researchers, industry practitioners, and government organizations. It is a technology-driven ecosystem, where better decision-making will help many organizations to extract knowledge from data in an interpretable and appropriate form. Strawn (2012), described Big Data as “fourth paradigm of science”, whereas (Hagstrom, 2012) defined it as “new paradigm of knowledge assets”, or “the next frontier for innovation, competition, and productivity” (Manyika et al., 2011). Gantz and Reinsel, (2011) defined Big Data as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling the high-velocity capture, discovery, and analysis”. It was described an integrated approach to organize, process, analyze the six characteristics (namely volume, variety, velocity, veracity, valence, and value). BDA is used to generate action for delivering the insights, value, measuring performance, and establishing competitive advantages (Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015). The paper by (De Mauro, Greco, & Grimaldi, 2016) defined that “Big Data is the information asset characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value” . The BDA, as a scientific topic of investigation, provides some significant and insightful readings that are discovered by various researchers. However, it is still needed to carryout the systematic review of innovative analytical methods, techniques, and tools for making insightful decisions in various domains. Indeed, it became a key component of decision-making processes in business (Hagel, 2015). The big data and advanced data analytics techniques can be used for the development of analytical and computational models (Iqbal, Doctor, More, Mahmud, & Yousuf, 2017). There are still several research interest how to develop the infrastructure, apply various data mining and machine learning algorithms in different domains. The BDA is concerned with modern statistical and machine learning techniques to analyze huge amount of data (Suthaharan, 2014). The researchers suggested that Big Data Analytics and deep learning have the potential to provide new generation applications based on modeling and simulation (Chen & Lin, 2014; Tolk, 2015). The traditional tools are not able to address the issues of scalability, adaptability, and usability, whereas such issues are critical to its success as they influence how big data is developed, managed and analyzed. The BDA is categorized by the requirement of advanced data acquisition, data storage, data management, data analysis, and visualization.