Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review

Miklošík, Andrej; Evans, Nina

doi:10.1109/access.2020.2998754

Cited by 56 publications

(24 citation statements)

References 56 publications

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“…The user can interrelate with the knowledge by providing the modules and personal preferences also in usability comments, by leveraging learning results that improves decision making, domain both can attend the a source of knowledge to central the learning process and the background of applying learned models. We concluded finally each and every components separately [13]. To propose a general purpose framework.…”

Section: Framework Of Big Data On Machine Learningmentioning

confidence: 99%

Big Data on Machine Learning – A Review

Balasree¹,

Dharmarajan²

2021

ESIJ

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In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

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Section: Framework Of Big Data On Machine Learningmentioning

confidence: 99%

Big Data on Machine Learning – A Review

Balasree¹,

Dharmarajan²

2021

ESIJ

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“…The implementation of DT has been considered in many industries, including marketing [6], smart cities [7], and energy [8]. In particular, digitalizing the supply VOLUME 4, 2016 chain and logistics service industry is recognized as one of the ten successful factors of Industry 4.0 [9] as digitalization creates a major improvement not only in the business models but also in the values to meet the increasing demands of customers and enhance customer experiences [10].…”

Section: Introductionmentioning

confidence: 99%

The Physical Internet in the Era of Digital Transformation: Perspectives and Open Issues

Tran-Dang

Kim²

2021

IEEE Access

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The Physical Internet (PI, or π) paradigm has been developed for a global logistics system that aims to move, handle, store, and transport logistics products in a sustainable and efficient way. To achieve this goal, the PI requires a higher level of interconnectivity and interoperability in terms of physical, informational, and operational aspects, which, by following the principle of the digital Internet (DI), is enabled by an interconnected network of intermodal hubs, collaborative protocols, and standardized, modular, and smart containers. Meanwhile, digital transformation (DT) has become mainstream in Industry 4.0 to innovate many industries, including logistics and supply chains, through the use of breakthrough digital technologies in the fields of information, communication, connectivity, analytics, and computing, such as the next generation of communication and networking (i.e., 5G), the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), big data analytics (BDA), and cloud computing (CC). In this context, the introduction of DT in the PI vision has many implications for the development and realization of an efficient and sustainable global logistics system. This study investigated the perspectives of PI under the impact of DT. The major challenges and associated open research regarding the adoption of DT in PI have been thoroughly investigated.

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“…One category of IS with increasing capabilities are artificial intelligence (AI)-powered systems. Such systems have many potential applications in decision making support, production automation, learning, communication, etc [9]. The communication between online users and organisations is shifting towards interactions with AI-driven systems [10].…”

Section: Introductionmentioning

confidence: 99%

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

2021

Self Cite

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Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review

Abstract: This paper is an output of the research project KEGA 016EU-4/2019 The innovative learning texts from marketing for secondary schools.

Cited by 56 publications

References 56 publications

Big Data on Machine Learning – A Review

Big Data on Machine Learning – A Review

The Physical Internet in the Era of Digital Transformation: Perspectives and Open Issues

The Use of Chatbots in Digital Business Transformation: A Systematic Literature Review

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