This book will provide an integrated approach by combining data quality and process quality approaches for ensuring robust quality. When the two concepts are combined, industry can achieve rapid improvements with accurate results and decisions.
This book will provide an integrated approach by combining data quality and process quality approaches for ensuring robust quality. When the two concepts are combined, industry can achieve rapid improvements with accurate results and decisions.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Rajesh Jugulum, PhD, is the Informatics Director at Cigna. Prior to joining Cigna, he held executive positions in the areas of process engineering and data science at Citi Group and Bank of America. Rajesh completed his PhD under the guidance of Dr. Genichi Taguchi. Before joining the financial industry, Rajesh was at Massachusetts Institute of Technology where he was involved in research and teaching. He currently teaches at Northeastern University in Boston. Rajesh is the author/co-author of several papers and four books including books on data quality and design for Six Sigma. Rajesh is an American Society for Quality (ASQ) Fellow and his other honors include ASQ's Feigenbaum medal and International Technology Institute's Rockwell medal. Rajesh has delivered talks as the keynote speaker at several conferences, symposiums, and events related to data analytics and process engineering. He has also delivered lectures in several universities/companies across the globe and participated as a judge in data-related competitions.
Inhaltsangabe
Chapter 1 The Importance of Data Quality and Process Quality Chapter 2 Data Science and Process Engineering Concepts Chapter 3 Building Data and Process Strategy and Metrics Management Chapter 4 Robust Quality-An Integrated Approach for Ensuring Overall Quality Chapter 5 Robust Quality for Analytics Chapter 6 Case Studies Appendix I: Control Chart Equations and Selection Approach Appendix II: Orthogonal Arrays Appendix III: Mean Square Deviation (MSD), Signal-to-Noise Ratio (SNR), and Robust Quality Index (RQI)
Chapter 1 The Importance of Data Quality and Process Quality Chapter 2 Data Science and Process Engineering Concepts Chapter 3 Building Data and Process Strategy and Metrics Management Chapter 4 Robust Quality-An Integrated Approach for Ensuring Overall Quality Chapter 5 Robust Quality for Analytics Chapter 6 Case Studies Appendix I: Control Chart Equations and Selection Approach Appendix II: Orthogonal Arrays Appendix III: Mean Square Deviation (MSD), Signal-to-Noise Ratio (SNR), and Robust Quality Index (RQI)
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