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Engineering Data Quality Automation, Validation, and Trust in Large-Scale Data Systems for Sustainable Industrial Processes
Abstract
In the era of sustainable industrial processes, the reliability and quality of data play a pivotal role in driving informed decision-making, process optimization, and regulatory compliance. Large-scale data systems, however, face challenges related to data heterogeneity, volume, velocity, and veracity. This chapter explores engineering practices for ensuring high-quality data through automation, validation, and trust mechanisms within data pipelines. Emphasis is placed on designing automated data quality checks, implementing real-time validation frameworks, and embedding trust through provenance tracking and governance-by-design principles. The chapter also highlights the role of emerging technologies such as AI-driven anomaly detection, blockchain-based auditability, and IoT-enabled monitoring for enhancing transparency in industrial workflows. A case study with quantitative results demonstrates how automated data quality engineering significantly improves operational efficiency, reduces compliance risks, and advances sustainability goals. By aligning data quality strategies with industrial sustainability objectives, the chapter provides a holistic framework for building resilient, trustworthy, and scalable data systems that enable industries to achieve both economic and environmental performance targets.
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