Robust Quality: Powerful Integration of Data Science and Process Engineering

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.  Read more... Abstract: 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 Content: Cover Half Title Title Page Copyright Page Table of Contents Foreword Preface Acknowledgments Author Chapter 1: The Importance of Data Quality and Process Quality 1.1 Introduction 1.2 Importance of Data Quality Implications of Data Quality Data Management Function 1.3 Importance of Process Quality Six Sigma Methodologies Development of Six Sigma Methodologies Process Improvements through Lean Principles Process Quality Based on Quality Engineering or Taguchi Approach 1.4 Integration of Process Engineering and Data Science for Robust Quality. Chapter 2: Data Science and Process Engineering Concepts2.1 Introduction 2.2 The Data Quality Program Data Quality Capabilities 2.3 Structured Data Quality Problem-Solving Approach The Define Phase The Assess Phase Measuring Data Quality Measurement of Data Quality Scores The Improve Phase The Control Phase 2.4 Process Quality Methodologies Development of Six Sigma Methodologies Design for Lean Six Sigma Methodology 2.5 Taguchi's Quality Engineering Approach Engineering Quality Evaluation of Functional Quality through Energy Transformation. Understanding the Interactions between Control and Noise FactorsUse of Orthogonal Arrays Use of Signal-to-Noise Ratios to Measure Performance Two-Step Optimization Tolerance Design for Setting up Tolerances Additional Topics in Taguchi's Approach Parameter Diagram Design of Experiments Types of Experiments 2.6 Importance of Integrating Data Quality and Process Quality for Robust Quality Brief Discussion on Statistical Process Control Chapter 3: Building Data and Process Strategy and Metrics Management 3.1 Introduction 3.2 Design and Development of Data and Process Strategies. 3.3 Alignment with Corporate Strategy and Prioritizing the Requirements3.4 Axiomatic Design Approach Design Axioms Designing through Domain Interplay Functional Requirements-Design Parameters Decomposition-Data Innovation Functional Requirements Design Parameters Functional Requirements-Design Parameters Decomposition-Decision Support Functional Requirements Design Parameters Functional Requirements-Design Parameters Decomposition-Data Risk Management and Compliance Functional Requirements Design Parameters. Functional Requirements-Design Parameters Decomposition-Data Access ControlFunctional Requirements Design Parameters End-to-End Functional Requirements-Design Parameters Matrix 3.5 Metrics Management Step 1: Defining and Prioritizing Strategic Metrics Step 2: Define Goals for Prioritized Strategic Metrics Step 3: Evaluation of Strategic Metrics Common Causes and Special Causes Step 4: Discovery of Root Cause Drivers Chapter 4: Robust Quality-An Integrated Approach for Ensuring Overall Quality 4.1 Introduction.