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Full Description
Historically, the term quality was used to measure performance in the context of products, processes and systems. With rapid growth in data and its usage, data quality is becoming quite important. It is important to connect these two aspects of quality to ensure better performance. This book provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.
Features:
Integrates data science, analytics and process engineering concepts
Discusses how to create value by considering data, analytics and processes
Examines metrics management technique that will help evaluate performance levels of processes, systems and models, including AI and machine learning approaches
Reviews a structured approach for analytics execution
Contents
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)
