IoT and Real-Time Data Analytics: Transforming Business Decision-Making Processes

Authors

  • Sreedhara Ramesh Chandra, Areman Ramyasri, Girish Kumar Kuppireddy, Anumula Sruthi, Yerravarapu VV Durga Prasad, Obulesu Varikunta

Keywords:

Internet of Things (IoT), Real-Time Data Analytics, Business Decision-Making, Predictive Maintenance, Smart Manufacturing, Personalized Marketing

Abstract

The integration of Internet of Things (IoT) technologies with real-time data analytics is revolutionizing business decision-making processes across various industries. IoT devices, which range from simple sensors to complex machinery, generate vast amounts of data that, when analyzed in real-time, can provide critical insights and facilitate more informed and timely decisions. This transformation is driven by the ability to monitor operations continuously, predict maintenance needs, optimize resource allocation, and enhance customer experiences through personalized services.

This abstract explores how businesses are leveraging IoT and real-time data analytics to gain a competitive edge. It delves into specific applications such as supply chain management, predictive maintenance, smart manufacturing, and personalized marketing. Furthermore, it examines the challenges businesses face in implementing these technologies, including data security concerns, the need for scalable infrastructure, and the integration of diverse data sources.

The findings highlight that companies embracing IoT and real-time analytics are not only improving operational efficiency and reducing costs but also driving innovation and creating new revenue streams. As the technology evolves, its role in strategic decision-making is becoming increasingly prominent, underscoring the importance for businesses to invest in and adapt to these advancements to stay relevant in a rapidly changing market landscape.

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References

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Published

30.06.2024

How to Cite

Sreedhara Ramesh Chandra. (2024). IoT and Real-Time Data Analytics: Transforming Business Decision-Making Processes. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 107 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6400

Issue

Vol. 12 No. 22s (2024)

Section

Research Article

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