Enterprise Data Quality Management: A Technical Deep Dive into Automated Governance

This comprehensive article examines the implementation and impact of enterprise-wide data quality management and governance frameworks in modern organizations. The article investigates how automated data quality monitoring, combined with robust governance protocols, transforms organizational data integrity and decision-making capabilities. Through details of various case studies and research findings, this article demonstrates the effectiveness of systematic approaches to data quality management, highlighting improvements in operational efficiency, compliance adherence, and financial performance. The article particularly focuses on the integration of machine learning capabilities, microservices architecture, and advanced analytics in enhancing data quality management systems, while also addressing the challenges and opportunities presented by modern enterprise data environments.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-04-02
DOI
https://doi.org/10.71097/ijsat.v16.i2.3050
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
Controls
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article

Enterprise Data Quality Management: A Technical Deep Dive into Automated Governance

Suman Etikala
1 citations
International Journal on Science and Technology
Big Data and Business Intelligence
2.28
article

Enterprise Data Quality Management: A Technical Deep Dive into Automated Governance

Suman Etikala
article en
1 citations

Abstract

This comprehensive article examines the implementation and impact of enterprise-wide data quality management and governance frameworks in modern organizations. The article investigates how automated data quality monitoring, combined with robust governance protocols, transforms organizational data integrity and decision-making capabilities. Through details of various case studies and research findings, this article demonstrates the effectiveness of systematic approaches to data quality management, highlighting improvements in operational efficiency, compliance adherence, and financial performance. The article particularly focuses on the integration of machine learning capabilities, microservices architecture, and advanced analytics in enhancing data quality management systems, while also addressing the challenges and opportunities presented by modern enterprise data environments.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 13%
Big Data and Business Intelligence
2.28
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