Cloud-Based Enterprise Data Quality Optimization using Semantic Validation and DataOps Pipeline Automation
DOI:
https://doi.org/10.21590/ijtmh.11.04.15Keywords:
Cloud computing, Enterprise data quality, Semantic validation, DataOps, Data pipeline automation, Metadata management, Machine learning, Data governance, Cloud-native architecture, Data consistency, Ontology-based validation, Big data analytics, Real-time monitoring, Enterprise automation, Knowledge graphs]Abstract
Modern enterprises generate massive amounts of structured and unstructured data through cloud platforms, IoT systems, enterprise applications, and digital services. Maintaining data quality in such dynamic environments has become a major challenge due to inconsistencies, duplication, missing values, and integration issues. Traditional data quality management techniques are often inadequate for modern cloud-native ecosystems because they lack scalability, contextual intelligence, and automation capabilities. This research proposes a cloud-based enterprise data quality optimization framework integrating semantic validation and DataOps pipeline automation. Semantic validation uses ontologies, metadata models, and knowledge graphs to verify contextual correctness and business consistency of enterprise data. DataOps automation enables continuous integration, automated testing, monitoring, and deployment of data pipelines for real-time quality management. The framework also incorporates machine learning algorithms for anomaly detection and predictive quality analysis. Cloud-native technologies such as microservices and container orchestration improve scalability and operational efficiency. The proposed approach enhances data accuracy, consistency, governance, interoperability, and decision-making capabilities across enterprise environments. Experimental evaluation demonstrates that combining semantic technologies with automated DataOps workflows significantly improves enterprise data reliability while reducing operational costs and manual intervention. The framework supports digital transformation initiatives by providing intelligent, scalable, and adaptive enterprise data quality management solutions.
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