AI-Enhanced Data Quality Control Mechanisms in Cloud-Based Data Engineering

Authors

  • Dillepkumar Pentyala 6303 Owensmouth Ave, Woodland Hills, CA 91367 Author

Keywords:

AI-enhanced Data Quality Control, Cloud-Based Data Engineering, Machine Learning for Anomaly Detection, Natural Language Processing in Data Cleansing, Automated Decision-Making Systems, Data Accuracy Improvement.

Abstract

In the era of big data and cloud computing, ensuring data quality has become a critical challenge for organizations aiming to leverage their data assets effectively. This paper presents a novel approach to data quality control through AI-enhanced mechanisms within cloud-based data engineering frameworks. By integrating advanced AI techniques with traditional data quality control methods, we propose a comprehensive framework that improves the accuracy, consistency, and reliability of data managed in cloud environments. The framework incorporates machine learning algorithms for anomaly detection, natural language processing for data cleansing, and automated decision-making systems to manage data quality metrics dynamically. The effectiveness of the proposed approach is demonstrated through extensive experiments using realworld datasets from various industries. Results indicate a significant reduction in data quality issues, with improvements in error detection rates and data accuracy. This paper provides a detailed analysis of the AI-driven mechanisms employed, evaluates their performance against conventional methods, and discusses the implications for data engineering practices in cloud computing environments. The findings contribute to advancing the field of data quality management by highlighting the benefits of integrating AI technologies into data quality control processes.

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Published

2019-12-23

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