Big Data Issues: Analytics and Security

Big Data Issues: Analytics and Security

Copyright: © 2025 |Pages: 23
DOI: 10.4018/978-1-6684-7366-5.ch020
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Big data is a well-known concept today, as most of the data on the internet is considered big data. Also, big data is concerned with data with a huge size, velocity, variety, and veracity. Big data presents many challenges, especially for companies on how to deal with it, regarding benefiting from it through the use analytics and at the same time preserving its security. So, this chapter covers many of the challenges that big data brings regarding analytics and security and how to find solutions to these two issues. And these solutions can be applied in real-life use cases.
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Big data is a collection of structured, semi-structured, and unstructured data that is gathered by businesses and may be mined to be used in advanced analytics applications using machine learning and predictive modelling. Big data is used by businesses to enhance operations, deliver better customer service, develop individualized marketing campaigns, and carry out other tasks that can ultimately boost sales and profits. Because they can act more quickly and with greater knowledge, businesses who use it efficiently may have a competitive advantage over those that don't. Data scientists and other data analysts need to have a thorough comprehension of the available data and a clear understanding of what they're looking for in it in order to provide reliable and pertinent results from big data analytics applications. As a result, a vital preparatory stage in the analytics process is data preparation, which involves profiling, cleansing, validation, and transformation of data sets. Securing Big Data, which frequently contains sensitive information, is another crucial concern that needs to be addressed.

This chapter is going to address two important issues for Big Data; which are analytics and security. Since, today companies and data scientists work on Big Data analytics to retrieve relevant information, and provide essential statistics to help companies and research labs in finding solutions to some questions, and improve their sales and competitiveness in the market, and at the same time, keeping these information secured. The objectives of this article are:

  • 1.

    Concentrating on Big Data security related issues

  • 2.

    Presenting various solutions to Big Data security challenges

  • 3.

    Clarifying importance of Big Data analytics, especially for companies

  • 4.

    Focusing on several solutions to issues related to Big Data security

  • 5.

    Providing strategies in general for managing effectively Big Data

  • 6.

    Discussing conclusion, and future research directions.

This chapter is organized as follows; the background represents the second section, where literature review is mentioned, the third section is composed of focus of the chapter, which is definition of Big Data, its importance, applications, and analytics, section 4 focuses on solutions and recommendations for Big Data security and analytics challenges, and finally, comes conclusion and future research directions.



Research needs to be done on two crucial Big Data issues: analytics and security. Some research on these two issues are mentioned in the following sections. Social networks, such as Facebook and Twitter are major producers of Big Data. Social networks like Facebook and Twitter are leading producers of big data. Social Network topology has strong impact on physical technological networks as most of the traffic is contributed by these social network sites and related ones. Some approaches and examples of the use of social network analysis in the design of technology networks and vice versa are explored in research (Cheng et al., 2013). A user can be a member of multiple networks at the same time, and these networks can combine to produce a composite social network in which the person's activity varies across networks. Moreover, the user may share similar latent interests with other users throughout these networks. E. Zhong (Zhong et al.,2012) proposed a model for adaptive transfer of knowledge from composite social networks to forecast human behavior for use in social marketing, service suggestions, and personalization. Using personal ad hoc clouds of users in social networks to address big data processing difficulties by leveraging the social network paradigm for generating information from big data was discussed in research (Tan et al., 2013). The combination of IoT, big data analytics, and complicated event processing techniques is suggested by authors (Tasweef et al., 2015) as a solution to the main problems with handling data in the healthcare industry. They proposed an all-encompassing healthcare system that could carry out tasks like drug detection, monitor patients from a distance, help with health insurance settlement, and advance the therapeutic outcomes.

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