Data Pipelines for Industrial AI Design, Integration, and Quality Assurance

Abstract

Industrial artificial intelligence depends not only on advanced algorithms, but also on the unseen data pipelines that feed, organize, validate, and sustain them. This chapter examines how data pipelines shape the design, integration, and quality assurance of AI-driven industrial systems. It explores the movement of data from sensors, machines, production lines, enterprise platforms, and human inputs into structured workflows capable of supporting reliable decision-making. Particular attention is given to data accuracy, interoperability, traceability, governance, and continuous monitoring. The chapter also considers the operational realities of industrial environments, where legacy systems, fragmented data sources, and production pressures often complicate implementation. By linking technical architecture with quality culture, the chapter argues that robust data pipelines are not merely digital infrastructure; they are the foundation upon which trustworthy industrial AI becomes practical, scalable, and meaningful.