Perspective Chapter: Quality Assurance in Unstructured Data – Challenges, Methods, and Practical Frameworks

Unstructured data, including texts, images, sounds, videos, logs, and sensor streams, dominate today’s information environment but are difficult to evaluate or regulate. This chapter goes beyond existing surveys by synthesizing diverse perspectives on quality assurance (QA) into a practical, modality-aware framework. We integrate classical dimensions of data quality (accuracy, completeness, consistency, reliability, timeliness, relevance) with regulatory and ethical requirements (e.g., General Data Protection Regulation, California Consumer Privacy Act, Lei Geral de Proteção de Dados, and Act on the Protection of Personal Information) and with emerging methods such as natural language processing, computer vision, foundation models, embedding-based similarity, and provenance standards (e.g., W3C PROV and FAIR). Our novel contribution is twofold: (i) We propose cross-cutting best practices that translate abstract standards (e.g., ISO/IEC 25012/25024, ISO 8000, and NIST AI RMF) into operational pipelines for unstructured data; and (ii) we provide case-based lessons from healthcare, manufacturing, Internet of Things, finance, e-commerce, and public data platforms to demonstrate implementation patterns. The chapter concludes with actionable recommendations for explainable, retrieval-augmented, multimodal, and privacy-preserving QA. This synthesis establishes a foundation for both researchers and practitioners to manage unstructured data quality at scale and under evolving governance demands.