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Big Data Analytics: Significance, Challenges and Techniques

Chatura Chinthana Gamage in Information Sciences

International Journal of Applied Information Systems
Year of Publication:2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Chatura Chinthana Gamage
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  1. Chatura Chinthana Gamage. Big Data Analytics: Significance, Challenges and Techniques. International Journal of Applied Information Systems 12(29):21-29, May 2020. URL, DOI BibTeX

    	author = "Chatura Chinthana Gamage",
    	title = "Big Data Analytics: Significance, Challenges and Techniques",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2020",
    	volume = 12,
    	number = 29,
    	month = "May",
    	year = 2020,
    	issn = "2249-0868",
    	pages = "21-29",
    	url = "",
    	doi = "10.5120/ijais2020451858",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Big data analytics have been embraced as a novel technology that will reshape domains such as business intelligence, cyber security and economic development that relies on data analytics to gain insights for better decision-making. In recent years, the rapid development of Internet, Information Systems, and Cloud Computing have led to the explosive growth of data in almost every industry and business area. Due to the rapid growth of such data, big data analytics techniques need to be explored and provided in order to process and derive value and knowledge from large datasets. Analysis of these data requires a lot of efforts at multiple levels of knowledge extraction for effective decision making. This paper aims to briefly introduce the concept of big data, analyze some of the different analytics methods and tools which can be applied to big data, as well as critically evaluate the significance of the big data analytics and challenges associated with the application of big data analytics in various decision domains.


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Big data, big data analytics, data mining, hadoop, analytical complexity, data visualisation