Data Quality Management in the Data Age
Description
This book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality.
High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services.
Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.
Reviews / Votes
"Professor Haiyan Yu's Data Quality Management in the Data Age is a timely and indispensable resource, rigorously addressing the foundational challenge of the AI era. The work is built upon the compelling central premise that "high-quality data is the cornerstone of AI development." The book excels at integrating the multifaceted nature of data with the logic of intelligence-driven decision-making. A major highlight is the innovative "Total Data Quality Management" (TDQM) framework. The TDQM framework establishes a comprehensive, four-dimensional management system that spans the entire data lifecycle. By standardizing the dimensions of data quality, the book meticulously formulates a data quality-value transformation function. Furthermore, Professor Yu introduces a quantitative model designed to accurately assess the impact of data bias on organizational decision-making. Overall, this book is a remarkably insightful, methodologically robust, and exceptionally well-structured contribution to the field." (Professor Robin Qiu, Professor in Information Science, Division of Information Science & Engineering, Pennsylvania State University, USA)
"Professor Haiyan Yu's Book, Data Quality Management in the Data Age, starts from a clear and practical idea: AI systems are only as good as the data that feeds them. The book shows, step by step, how people who collect, buy, govern, or use data can raise its quality and improve the decisions they make. At the heart of the book is a Total Data Quality Management (TDQM) framework. It adapts familiar quality-management thinking to modern data work and follows the full data lifecycle. For readers with general backgrounds, the value lies in the author's habit of translating methods into actions. Two ideas will be beneficial to practitioners. First, the book connects data quality to value. Second, it offers a practical treatment of bias. A telecommunications case study illustrates how data certification can legitimately raise the quality of data products and value for users." (Professor Ping Yu, School of Computing and Information Technology, University of Wollongong, Australia)
"In Data Quality Management in the Data Age, Professor Haiyan Yu presents a compelling premise: high-quality data is the cornerstone of effective Artificial Intelligence and modern digital innovation. The book successfully bridges traditional quality management theory with the practical demands of data science, introducing a comprehensive Total Data Quality Management framework grounded in the principle that data quality drives value creation. This framework spans the entire data lifecycle-collection, governance, circulation, and application-integrating statistical process control and dynamic rules to quantify how improvements in data quality enhance asset value. A well-designed case study on telecommunications data transactions further demonstrates how data quality certification can directly influence pricing and market value. With its innovative insights, logical clarity, and exemplary real-world cases, this book represents a meaningful contribution." (Professor Ehsan Hajizadeh, Assistant Professor, Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, Iran)
"Professor Haiyan Yu's Data Quality Management in the Data Age is a timely and insightful contribution grounded in the premise that "high-quality data is the cornerstone of AI development." The book systematically examines how to construct, evaluate, and leverage quality data to support intelligent decision-making. A central innovation of the book is the Total Data Quality Management framework, which integrates traditional quality management principles with modern data science. Guided by the idea that "data quality drives value creation," the framework establishes a four-dimensional lifecycle-collection, governance, circulation, and application-and introduces a data quality-value transformation function to quantify how improvements in data quality enhance the value of data assets. Through rigorous analysis and a detailed telecommunications case study, Professor Yu demonstrates how data bias can influence decision-making and how data quality certification can affect market pricing. This book offers a valuable framework for advancing data-driven innovation." (Dr. Xuewen Lu, Professor in Statistics/Biostatistics, University of Calgary)
"Professor Haiyan Yu's Data Quality Management in the Data Age establishes that "high-quality data is the cornerstone of AI development," systematically exploring pathways to construct and sustain such data. The work integrates data's economic and cognitive attributes with intelligence-driven decision logic, analyzing quality management methodologies and data market mechanisms through the lenses of randomness, systematicity, and latency. The innovative "Total Data Quality Management" framework combines traditional quality theory with modern data science. Centered on "data quality drives value creation," it establishes a four-dimensional system spanning the complete data lifecycle. By standardizing quality dimensions and incorporating statistical process control with dynamic modeling, the book develops a quality-value transformation function clarifying quality's marginal effects on data valuation. A quantitative model assessing data bias impact on decisions is validated through real-world cases demonstrating quality certification's leverage on pricing. This book is a methodological work balancing theoretical depth with practical relevance." (Dr. Fanwen Meng, Assistant Director, Health Services and Outcomes Research, NHG Health, Singapore)
"Professor Haiyan Yu's Data Quality Management in the Data Age establishes a compelling foundation by asserting that "high-quality data is the cornerstone of AI development," and systematically examines pathways to construct and sustain such data. The work integrates data's multifaceted attributes as both an economic and cognitive factor with intelligence-driven decision logic. A notable contribution is the innovative "Total Data Quality Management" framework, which synthesizes traditional quality management theory with modern data science. By standardizing data quality dimensions and incorporating experimental design, statistical process control, and dynamic rule modeling, the book develops a data quality - value transformation function that clarifies the marginal effects of quality improvement on data asset valuation. This book is a timely, insightful, and methodologically robust work that successfully bridges theoretical depth and practical applicability." (Professor Qingpeng Zhang, Associate Professor, Institute of Data Science, The University of Hong Kong, Hong Kong S.A.R., China)
"Professor Yu's book systematically charts a path to high-quality data. Uniting the three lenses of randomness, systemacity and "ghost" data, it sets out a full-cycle data-quality methodology, measurement toolkit and market mechanism, and advances an integrated "Total Data Quality Management" framework. In an era when big data and AI are rapidly converging, the work offers both a practical blueprint for building premium datasets and methodological guidance for domain-specific knowledge bases-including the integration of Chinese and Western medicine." (Translated from Chinese, Professor Xiaohua Zhou, Chair Professor of Peking University, Dean of Department of Biostatistics, Peking University, Vice president of Peking University Chongqing Big Data Research Institute)
"Prof. Yu's book examines the convergence of artificial intelligence and the real economy through the lens of new-quality productive forces and the digital economy. Centering on the thesis that high-quality data are the core carrier of this convergence, it presents a systematic methodology for building premium data sets and unlocking the value of data as a factor of production. The book introduces a quantitative model that measures how data bias skews decisions and, drawing on real-world data-trading cases, demonstrates the pivotal role of quality certification in data pricing-thereby furnishing a robust foundation for building an efficient data-product and service ecosystem." (Translated from Chinese, Professor Zhen He, Chair Professor of Tianjin University, academician of the International Academy of Quality Sciences)
"Professor Yu's book centers on the thesis that high-quality data are the pivotal carrier for fusing AI with the real economy, and it systematically charts the route to creating such data. Its framework offers indispensable guidance for assembling premium data sets and energizing vibrant, trustworthy data markets." (Translated from Chinese, Jianming Zhou, member of the Standing Committee of the communication science and Technology Commission of the Ministry of industry and information technology and former president of China Mobile Research Institute)
"Professor Haiyan Yu starts from the premise that "high-quality data are the bedrock of artificial-intelligence progress." He then maps, step by step, how to create such data, weaving an original logic that treats data as a factor of production and couples every data attribute to technology-driven decisions. In France-where academia and industry alike are scrambling to assemble reliable data sets-the book is already a key reference. Its methodology is especially prized in health-management and data-intelligence circles, where rigorously curated data are indispensable for guiding strategy, streamlining care pathways, and generating lasting value." (Yuchen Lei, MSc in Health Management & Data Intelligence EMLYON Business School, France)
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Person
Haiyan Yu is an Associate Professor at Chongqing University of Posts and Telecommunications (China). He obtained his Ph.D. from Tianjin University in 2015. Subsequently, he served as a Postdoctoral Fellow at the University of Electronic Science and Technology of China from 2016 to 2017, and at Pennsylvania State University (US) from 2017 to 2020. Additionally, he was a Visiting Scholar at Purdue University (US) from 2020 to 2021. His research interests include causal inference and machine learning, personalized medicine, quality management, constrained optimization, and clinical decision support systems.
Content
Introduction of data quality management.- Quality management in Data Science.- Pillars of data quality management.- Tools of data quality management.- Experimental designs for data quality control.- High-quality data collection in data markets.- Ghost data in data quality management.- Summary.
