Vol. 3 No. 1 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Enhancing Predictive Analytics with AI-Powered RPA in Cloud Data Warehousing: A Comparative Study of Traditional and Modern Approaches

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  • Jeshwanth Reddy Machireddy,
  • Harini Devapatla
Jeshwanth Reddy Machireddy
Sr. Software Developer, Kforce INC, Wisconsin, USA
Harini Devapatla
Automation Engineer Lead, Wisconsin, USA
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Published 18-05-2023

Keywords

  • Artificial intelligence,
  • Robotic Process Automation,
  • cloud data warehousing,
  • predictive analytics,
  • machine learning
Creative Commons License

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

How to Cite

[1]
J. Reddy Machireddy and H. Devapatla, “Enhancing Predictive Analytics with AI-Powered RPA in Cloud Data Warehousing: A Comparative Study of Traditional and Modern Approaches”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 1, pp. 74–99, May 2023, Accessed: Sep. 16, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/23

Abstract

The integration of artificial intelligence (AI) and Robotic Process Automation (RPA) within cloud data warehousing systems represents a significant advancement in the realm of predictive analytics. This paper presents a comprehensive comparative study of traditional data processing methodologies versus contemporary AI-powered RPA approaches, with a particular focus on their application within cloud environments. Traditional methods, often reliant on manual data handling and conventional scripting, have historically faced challenges related to inefficiencies, limited scalability, and suboptimal accuracy in predictive modeling. In contrast, AI-powered RPA promises enhanced efficiency, accuracy, and scalability by automating data preparation and feature engineering tasks.

This research delves into the core distinctions between these approaches, examining how AI-powered RPA can streamline workflows by automating repetitive tasks, thereby significantly reducing the time and cost associated with data preparation. The study analyzes various AI algorithms, such as machine learning and deep learning models, and their role in optimizing data processing pipelines within cloud data warehouses. By comparing these modern techniques with traditional approaches, the paper highlights the advantages and limitations of each, providing a nuanced understanding of how AI and RPA contribute to more accurate and scalable predictive analytics.

A key focus of this paper is the evaluation of operational cost reductions facilitated by AI-powered RPA. Traditional data processing methods often involve extensive human intervention and manual error correction, leading to increased operational costs and slower response times. AI-driven automation, on the other hand, minimizes these costs by reducing the need for manual oversight and accelerating the data processing cycle. The study also explores the impact of these technologies on decision-making processes across various industries, demonstrating how enhanced data accuracy and reduced processing times can lead to more informed and timely decisions.

In addition to the efficiency and cost benefits, this paper investigates the scalability of predictive models when utilizing AI-powered RPA. Scalability is a critical factor for modern data warehousing systems, which must handle increasingly large datasets and complex analytical tasks. The study examines how AI and RPA facilitate scalable solutions that can adapt to growing data volumes and evolving analytical requirements, offering insights into the future direction of data processing technologies.

This comparative analysis also includes practical case studies showcasing real-world implementations of AI-powered RPA in cloud data warehousing environments. These case studies provide empirical evidence of the effectiveness of AI and RPA in improving predictive analytics and operational efficiency. By contrasting these case studies with traditional methods, the paper elucidates the tangible benefits and challenges associated with each approach.

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