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Data Governance by Design: Building Compliant and Reliable Data Engineering Workflows for Sustainable Industrial Processes
Abstract
The increasing reliance on data-driven decision-making in industrial sectors has amplified the importance of robust, compliant, and reliable data engineering workflows. With the growing demand for sustainable industrial processes, organizations must ensure that their data systems are not only efficient but also aligned with governance principles that emphasize regulatory compliance, security, and ethical responsibility. This chapter explores a data governance by design approach, embedding governance mechanisms directly into the architecture of data pipelines and workflows. By focusing on automation, standardization, and traceability, the chapter demonstrates how organizations can achieve higher reliability and accountability in managing industrial data. The integration of governance practices is analyzed through key dimensions, including compliance with data protection regulations, quality assurance, lifecycle management, and sustainable data storage. Furthermore, the discussion highlights real-world use cases where data governance frameworks have enabled energy efficiency, reduced operational risks, and ensured long-term sustainability in industrial ecosystems. The chapter concludes by outlining best practices and strategies to future-proof data engineering workflows, enabling industries to balance innovation with compliance in the era of sustainable digital transformation.
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