Use of Big Data Tools and Industrial Internet of Things: An Overview

Wang, Yingzi; Jan, Muhammad Nazir; Chu, Sisi; Zhu, Yue

doi:10.1155/2020/8810634

Cited by 9 publications

(2 citation statements)

References 62 publications

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“…This approach helps enterprises innovate marketing strategies and effectively mine customer and market data. Big Data Tools in the Industrial Internet of Things: Wang et al (2020) explored the use of big data tools in the Industrial Internet of Things (IIoT), which is also relevant to marketing. The study provided a detailed report on techniques and tools for managing and organizing big data, extracting meaningful information, and their applications in various industries, including marketing.…”

Section: Big Data Analytics Tools and Techniques Overview Of Big Data...mentioning

confidence: 99%

Leveraging Big Data for Personalized Marketing Campaigns: A Review

Gold Nmesoma Okorie,

Zainab Efe Egieya,

Uneku Ikwue

et al. 2024

Int. j. manag. entrep. res

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The burgeoning field of big data analytics has revolutionized the landscape of marketing, offering unprecedented opportunities for personalized marketing campaigns. This review aims to synthesize the current state of knowledge on leveraging big data for personalized marketing, elucidating the objectives, methodologies, key findings, and conclusions drawn from recent research in this domain. The primary objective of this review is to explore how big data analytics can be effectively utilized to tailor marketing strategies to individual consumer preferences, behaviors, and patterns. Methodologically, the review adopts a comprehensive approach, examining a wide range of studies that employ various big data tools and techniques, including machine learning algorithms, data mining, and predictive analytics, in the context of personalized marketing. Key findings indicate that big data analytics significantly enhances the ability of marketers to understand and predict consumer behavior, leading to more effective targeting and segmentation strategies. The integration of big data has shown to improve customer engagement, satisfaction, and loyalty by delivering more relevant and timely marketing messages. However, challenges such as data privacy concerns, the need for advanced analytical skills, and the potential for data inaccuracies are also highlighted. In conclusion, while big data presents substantial opportunities for personalizing marketing campaigns, its effective implementation requires careful consideration of ethical implications, investment in technological infrastructure, and ongoing skill development. Future research directions include exploring the impact of emerging technologies like artificial intelligence and the Internet of Things (IoT) on personalized marketing, and developing frameworks for ethical data usage in marketing practices. This review underscores the transformative potential of big data in reshaping personalized marketing strategies, offering valuable insights for both practitioners and researchers in the field. Keywords: Big Data, Marketing Strategies, Consumer Behavior, Data Analytics, Personalized Marketing, Market Segmentation, Privacy Concerns, Ethical Challenges, Digital Transformation, Artificial Intelligence (AI).

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Section: Big Data Analytics Tools and Techniques Overview Of Big Data...mentioning

confidence: 99%

Leveraging Big Data for Personalized Marketing Campaigns: A Review

Gold Nmesoma Okorie,

Zainab Efe Egieya,

Uneku Ikwue

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

show abstract

“…The disparate nature of data cannot be processed with simple tools and techniques [2,5], and this creates a challenge for decision-makers to make decisions based on the scattered data. Emerging technologies, such as the Internet of Things (IoT), Industry 4.0 (I4.0), and extended reality (XR), produce distinct kinds of information via heterogeneous sources and real-world applications that create heterogeneity issues [6], in IoT integration, security, analytics challenges, and computational time [7][8][9]. Among them, data harmonization (DH), which describes the uniform representation of heterogeneous data, was proposed in References [10,11].…”

Section: Introductionmentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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Analyzing Frameworks for IoT Data Storage, Representation and Analysis: A Statistical Perspective

Purbey

Khandelwal

2021

Lecture Notes in Networks and Systems

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Use of Big Data Tools and Industrial Internet of Things: An Overview

Cited by 9 publications

References 62 publications

Leveraging Big Data for Personalized Marketing Campaigns: A Review

Leveraging Big Data for Personalized Marketing Campaigns: A Review

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

Analyzing Frameworks for IoT Data Storage, Representation and Analysis: A Statistical Perspective

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