Data Quality Management in the Data Age : Excellence in Data Quality for Enhanced Digital Economic Growth

• Record Nr.: UNINA9910900173703321
• Autore: Yu Haiyan
• Pubbl/distr/stampa: Cham : , : Springer, , 2024; ©2024
• ISBN: 9783031718717; 3031718712
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (103 pages)
• Collana: SpringerBriefs in Service Science Series
• Soggetti: Data editing; Knowledge economy
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Preface -- Background and Significance -- Acknowledgments -- Contents -- About the Author -- List of Figures -- List of Tables -- Chapter 1: Introduction of Data Quality Management -- 1.1 Introduction -- 1.1.1 Data Quality Issues -- 1.1.2 Concepts of Data Quality -- 1.1.3 Structure of This Book -- 1.2 Brief Overview of Data Quality Management -- 1.3 Development of Data Quality Management -- 1.3.1 Deming and Quality Management -- 1.3.2 Progress of Quality Management -- 1.4 Concept of Data Quality Management -- 1.4.1 Definition of Data Quality -- 1.4.2 Components of Data Quality Control -- 1.5 Impact of Data Quality in Data Markets -- 1.5.1 Impact of DQ on AI Performance -- 1.5.2 Impact of DQ on Treatment Effects Identification -- 1.5.3 Impact of DQ on Data Exchange and Transaction -- References -- Chapter 2: Quality Management in Data Science -- 2.1 The Evolution of Quality Management -- 2.1.1 Development of Statistics -- 2.1.2 Evolution from Probability to Data Computation -- 2.1.3 Data Is Power -- 2.1.4 Divide and Conquer
• Sommario/riassunto: Haiyan Yu's book, 'Data Quality Management in the Data Age,' focuses on the critical role of high-quality data in driving digital economic growth. It explores the necessity of data markets to procure high-; quality data and the challenges of managing data quality, particularly in data-scarce fields like personalized medicine. The book outlines the dimensions and metrics of data quality, the impact of low-quality data, and presents theories on quality management in data science. It is tailored for data scientists, engineers, data market managers, researchers, and graduate students in quality engineering and service science. By enhancing data quality management skills, the book aims to improve the data market environment, encouraging the participation of data sellers with high-quality data. It also discusses recent advancements in statistical quality control methods and offers a case study on data quality management.