A survey towards an integration of big data analytics to big insights for value-creation

By Mandeep Kaur Saggi, Sushma Jain. Information Processing & Management, 2018

377 citations recorded by the source corpus.

Abstract 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 decision-making. 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.

Subfields: Management Information Systems, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/fdf5033947321dd1a89975eb67ba1ed6ed0eefc8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

1. Yogesh Kumar Dwivedi
2. D. L. Hughes
3. K. Raman
4. Elvira R. Ismagilova

In Management Science and Operations Research

1. Mohsen Guizani
2. Yogesh Kumar Dwivedi
3. D. L. Hughes
4. K. Raman
5. Elvira R. Ismagilova
6. Nripendra P. Rana

In Marketing

1. Yogesh Kumar Dwivedi
2. D. L. Hughes
3. K. Raman
4. Elvira R. Ismagilova

Citations in the corpus

• Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “To this end, a few promising platforms can be named as Hadoop, Spark, Flink, Beam etc [191].”
• Smart cities: Advances in research - An information systems perspective (2019, methodology): “…Albalas et al. (2018) Data processing Hashem et al.(2016), Honarvar and Sami (2016), Hsu and Li (2018), Janssen and Kuk (2016), Russell (2012), Saggi and Jain (2018), Okwechime et al. (2017), Oßner et al. (2016), Pandithurai and Suresh Kumar (2016), Lazerson et al. (2018) Data storage Huang…”
• A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “To obtain insights from these data, researchers [39-44] have discussed different methods of integrating big data analytical methods with IoT design.”
• The Role of Big Data Analytics in Industrial Internet of Things (2019, neutral): “Although IIoT [19–24] and BDA [13, 25–31] have been widely studied separately, only a few studies including [32] have explored the convergence of the two domains.”