Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Analyzing IoT Data: Efficient Pipelines for Insight Extraction

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  • Sairamesh Konidala
Sairamesh Konidala
Vice President at JPMorgan & Chase, USA
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Published 21-07-2023

Keywords

  • Data Pipelines,
  • IoT Data Analysis
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Sairamesh Konidala, “Analyzing IoT Data: Efficient Pipelines for Insight Extraction”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 683–707, Jul. 2023, Accessed: Jan. 02, 2025. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/266

Abstract

The rapid adoption of the Internet of Things (IoT) has led to an unprecedented influx of data generated by connected devices across various industries. To harness the full potential of IoT, businesses and organizations must efficiently process, analyze, and extract meaningful insights from this continuous stream of data. This paper explores efficient data pipelines designed to handle the scale, velocity, and variety of IoT data while ensuring timely and accurate analytics. We discuss the challenges posed by IoT data, including real-time processing, data integration, and handling diverse data formats from sensors, smart devices, and industrial equipment. The focus is on developing scalable architectures that optimize data ingestion, transformation, storage, and analysis, enabling actionable insights for decision-making. These pipelines aim to reduce latency and improve reliability in data analytics workflows by leveraging distributed computing, edge processing, and cloud infrastructure. Given the sensitive nature of many IoT applications, we also highlight strategies to manage data quality, ensure security, and maintain privacy. Practical use cases from smart cities, healthcare, manufacturing, and logistics industries demonstrate the value of well-designed data pipelines in improving operational efficiency, predicting maintenance needs, and enhancing customer experiences. Ultimately, this exploration underscores the importance of streamlined, efficient pipelines in making sense of the overwhelming data produced by IoT ecosystems. Organizations can unlock powerful insights, drive innovation, and remain competitive in a data-driven world by adopting effective data processing techniques and scalable infrastructure.

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