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.
- 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
Table of 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)
About the Series
Continuous Improvement refers to all aspects of improving processes, products, methodologies, and techniques to ensure they provide more value to customers and are sustainable. This entails topics such as six sigma, lean, design for six sigma, quality, preventative and predictive maintenance and sustainability. Continuous improvement involves using various methodologies to design and measure changes to document the quantitative and qualitative improvements and sustain the gains. This involves breakthrough thinking and changing the culture of an organization. This series will cover the methodological and cultural aspects of driving continuous improvements in all types of organizations for sustainable results. The concepts behind continuous improvement will ensure companies innovate and become the best in class with successful financial impacts.
BISAC Subject Codes/Headings:
- BUSINESS & ECONOMICS / General
- COMPUTERS / Database Management / Data Mining
- TECHNOLOGY & ENGINEERING / Quality Control