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Data Quality Matters
Rohit K. Ramakrishnan, Priya Chandran, Adriana Marotta, Flavia Serra, Katja Berčič, Vágner Anikó, Marta Zorrilla, Ricardo Dintén, Juan Yebenes, Eduardo Vyhmeister, Bastien Pietropoli, Andrea Visentin, Yoram Segal, Adi Hod, Ginanjar Wiro Sasmito, Emre Akadal, Daniel Spichtinger, Rebecca E. Ghosh, Rachael Williams, Puja Myles, Pavel Hrubeš, Martin Langr, Zuzana Purkrábková, Hugo Guedes, Ana Rita Barata, Célia Domingues, Paula Teixeira, Gonçalo Almeida, Teresa Nogueira, Achilleas Marinakis, Christos A. Gizelis, Michalis Kefalogiannis, Nikolaos Tiniakos, Stavrina Sabati, Vrettos Moulos, Jordi Arjona Aroca, María José López Osa, Idoia Murua Belacortu, Efthymios Chondrogiannis, Efstathios Karanastasis, M. Eduard Tudoreanu, Mahalakshmi Sakthivel, Sergio Zepeda, Juan C. Estrada, Daniel Estrada, Dinh Tuan Tran, Ning Wang, Seong-Gyun Leem, Radziah Mohamad, Junhan Li, Tan Ser Xuen, Johanna Ahmad, Alif Ridzuan Khairuddin, Seyed Muhammad Hossein Mousavi, Orhan Ermis, Djamel Khadraoui, Esteban García-Cuesta, Alba Maria López, David Medina-Ortiz, Sebastián Escobedo, Norma Murillo-Acevedo, Nicole Soto-García, Diego Fernández-Villegas, Diego Sandoval, Anamaría Daza, Theodora Gazi, Alexandros Gazis, Sebastian Ventura (editor), José M. Luna (editor), Antonio R. Moya Martín-Castaño (editor)
2026
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Data quality has become a defining factor in the reliability, legitimacy, and impact of data-driven organisations. As data ecosystems grow in scale and complexity, encompassing heterogeneous sources, unstructured data, advanced analytics, and artificial intelligence, the consequences of poor data quality extend well beyond technical inefficiencies. Inconsistent, incomplete, or poorly governed data can undermine decision-making, weaken confidence in automated systems, and increase regulatory, ethical, and reputational risks. Data Quality Matters - Best Practices for Integrity and Assurance offers a comprehensive and timely exploration of these challenges, presenting data quality as a socio-technical capability that must be addressed across the entire data lifecycle. Bringing together conceptual foundations, governance approaches, methodological techniques, and applied experiences, the volume moves beyond narrow interpretations of data quality as a set of isolated metrics. Instead, it emphasises integrity (the consistency, traceability, and soundness of data), and assurance (the organisational and technical mechanisms that justify confidence in data and data-driven outcomes). The book covers key topics such as data quality models and dimensions, data governance and policy frameworks, integration and harmonisation, regulatory and legal considerations, quality assurance for unstructured and synthetic data, and emerging challenges in data-centric artificial intelligence. Designed for researchers, practitioners, and decision-makers alike, this volume bridges theory and practice, offering both insight and guidance for translating data quality principles into operational capabilities. By addressing quality as a foundational enabler of trustworthy analytics and responsible AI, this book provides a valuable reference for those seeking to improve data-driven decisions in complex, real-world environments.
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Keywords
- analytical tools
- Big Data Quality
- Data Audit
- Data Ethics
- data governance
- Data Integrity Standards
- data quality dimensions
- GDPR Compliance
- implementation challenges
- innovative solutions
- quality metrics
- thema EDItEUR::U Computing and Information Technology::UN Databases
- unstructured data
Links
DOI: 10.5772/intechopen.1008579Editions