RESEARCH JOURNAL OF MASS COMMUNICATION AND INFORMATION TECHNOLOGY (RJMCIT )

E-ISSN 2545-529X
P-ISSN 2695-2475
VOL. 9 NO. 2 2023
DOI: https://doi.org/10.56201/rjmcit.v9.no2.2023.pg39.51


Big Data Features As Crucial Concept which Effects Data Interoperability

Sana Ibrahim Shatwan, Weiam S. Elsaghair


Abstract


Big data has become an exciting term in the last decades, and it conducts a significant rate of data with different features of velocity, variety, value, volume, and veracity. Those features describe big data identity. Think about the data generated by social media websites and apps, such as Facebook, Twitter, and WhatsApp…...Etc. This data is crucial because of the amount, variety, and growth acceleration, but it is exhausting to manage. Big data interoperability indicates the capability to interchange and share the data, information, and knowledge between organizations and devices that knowledge and information were extracted from different data sources. This complex mutual process grows an organization's performance. According to the previous, big data and its five features are essential because of its usability and analytics. Organizations need to extract the expected benefit from preserving and retrieving data. We develop a data interoperability concept, which is improved as data exchangeability can be enriched. In this study, we will discuss the impact of big data feature's value, volume, variety, velocity, and veracity on the interoperability of the data and data exchangeability feasibility and how these features affect the prediction of future decisions in the institution, as well as the interaction and exchange of data between the organizations


keywords:

Big data, Big data Interoperability, Big data features.


References:


A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and
analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, 2015, doi:
10.1016/j.ijinfomgt.2014.10.007.

A. De Mauro, M. Greco, and M. Grimaldi, “A formal definition of Big Data based on its
essential features,” Libr. Rev., vol. 65, no. 3, pp. 122–135, 2016, doi: 10.1108/LR-06-
2015-0061.

M. Ghasemaghaei and G. Calic, “Assessing the impact of big data on firm innovation
performance: Big data is not always better data,” J. Bus. Res., vol. 108, no. April 2019, pp.


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