Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

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Coding Inequality

Predict and Surveil ◽

10.1093/oso/9780190684099.003.0006 ◽

2020 ◽

pp. 100-117

Author(s):

Sarah Brayne

Keyword(s):

Decision Making ◽

Big Data ◽

Data Analytics ◽

Social Inequalities ◽

Big Data Analytics ◽

The Other ◽

Surveillance Network ◽

Data Intensive ◽

The One ◽

Reproduction Of Inequality

This chapter looks at the promise and peril of police use of big data analytics for inequality. On the one hand, big data analytics may be a means by which to ameliorate persistent inequalities in policing. Data can be used to “police the police” and replace unparticularized suspicion of racial minorities and human exaggeration of patterns with less biased predictions of risk. On the other hand, data-intensive police surveillance practices are implicated in the reproduction of inequality in at least four ways: by deepening the surveillance of individuals already under suspicion, codifying a secondary surveillance network of individuals with no direct police contact, widening the criminal justice dragnet unequally, and leading people to avoid institutions that collect data and are fundamental to social integration. Crucially, as currently implemented, “data-driven” decision-making techwashes, both obscuring and amplifying social inequalities under a patina of objectivity.

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A Closer Look at Direct Carrier Billing

International Journal of Online Marketing ◽

10.4018/ijom.2020100102 ◽

2020 ◽

Vol 10 (4) ◽

pp. 18-40

Author(s):

Lorena Herrera López

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Mobile Network ◽

Marketing Channels ◽

Digital Marketing ◽

Mobile Service ◽

Web Analytics ◽

Network Operator ◽

Over The Top

The impulse to digitalization by telecom operators requires the commercialization of over-the-top services (OTT) based on the fine understanding and prediction of customer behaviour through pattern recognition involving big data, resulting in an essential part of web analytics and digital marketing. The objective of this research is to analyse factors influencing the purchase and use of a mobile game commercialized by a mobile network operator (MNO), through different digital marketing channels and using direct carrier billing (DCB) as payment channel. The novelty contribution of this study is twofold. Firstly, it assesses determinants related to the purchase and use of a mobile service through the analysis of variables identified in the scientific literature's review. In addition, it also incorporates a set of variables based on data retrieved from big data analytics. Secondly, this research analyses the willingness of consumers to pay through DCB.

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