"In this book by Dr. Jugulum, readers will learn how data can be systematically collected and deduced. Recommend it highly." --Nam P Suh, Former President of Korea Advanced Institute of Science and Technology, Ralph E & Eloise F. Cross Professor Emeritus at MIT "It is common sense that without quality data there cannot be quality decisions . . . Rajesh does an excellent job of explaining step by step how to develop a data quality program and implement it." --Desh Deshpande, Entrepreneur, Life member MIT Corporation, Co-chair National Council for Innovation and Entrepreneurship QUALITY DATA…mehr
"In this book by Dr. Jugulum, readers will learn how data can be systematically collected and deduced. Recommend it highly." --Nam P Suh, Former President of Korea Advanced Institute of Science and Technology, Ralph E & Eloise F. Cross Professor Emeritus at MIT "It is common sense that without quality data there cannot be quality decisions . . . Rajesh does an excellent job of explaining step by step how to develop a data quality program and implement it." --Desh Deshpande, Entrepreneur, Life member MIT Corporation, Co-chair National Council for Innovation and Entrepreneurship QUALITY DATA MEANS QUALITY BUSINESS All over the world, organizations are under scrutiny for how they manage and handle the massive volumes of data that they collect and store. You can get ahead today with Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality, a comprehensive guide for professionals concerned with data quality issues and programs. In Competing with High Quality Data, Rajesh Jugulum takes you through the steps you can follow to vault your company to the next level of operational efficiency through data quality management. The book explains: * The data quality program and its four-phase approach: Define, Assess, Improve, and Control * How data quality can increase efficiencies and maximize organizational benefits * The effects of a poor or non-existent data quality program * Why data quality must be integrated with process quality * The importance of building an enterprise-wide data quality practices center With a four-phase approach, Jugulum shows you how to ensure that every data quality project follows these phases to: * Reduce costs * Reduce manual processing or rework * Improve reporting * Enhance revenue opportunities Prepare your company for the future of increased competition and enhanced regulation that demands quality data. With Competing with High Quality Data , get your data quality program in order before, rather than after, you make decisions that could affect the future of your organization.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
DR. RAJESH JUGULUM, Ph.D., is a Data Quality and Analytics Professional. Rajesh held executive positions in these fields at Citi Group and Bank of America. Before joining financial industry, Rajesh was with MIT where he was involved in research and teaching. Currently, he teaches at Northeastern University in Boston. His honors include 2002 American Society for Quality's Feigenbaum medal and 2006 International Technology Institute's Rockwell medal.
Inhaltsangabe
Foreword xiii Prelude xv Preface xvii Acknowledgments xix 1 The Importance of Data Quality 1 1.0 Introduction 1 1.1 Understanding the Implications of Data Quality 1 1.2 The Data Management Function 4 1.3 The Solution Strategy 6 1.4 Guide to This Book 6 Section I Building a Data Quality Program 2 The Data Quality Operating Model 13 2.0 Introduction 13 2.1 Data Quality Foundational Capabilities 13 2.1.1 Program Strategy and Governance 14 2.1.2 Skilled Data Quality Resources 14 2.1.3 Technology Infrastructure and Metadata 15 2.1.4 Data Profi ling and Analytics 15 2.1.5 Data Integration 15 2.1.6 Data Assessment 16 2.1.7 Issues Resolution (IR) 16 2.1.8 Data Quality Monitoring and Control 16 2.2 The Data Quality Methodology 17 2.2.1 Establish a Data Quality Program 17 2.2.2 Conduct a Current-State Analysis 17 2.2.3 Strengthen Data Quality Capability through Data Quality Projects 18 2.2.4 Monitor the Ongoing Production Environment and Measure Data Quality Improvement Effectiveness 18 2.2.5 Detailed Discussion on Establishing the Data Quality Program 18 2.2.6 Assess the Current State of Data Quality 21 2.3 Conclusions 22 3 The DAIC Approach 23 3.0 Introduction 23 3.1 Six Sigma Methodologies 23 3.1.1 Development of Six Sigma Methodologies 25 3.2 DAIC Approach for Data Quality 28 3.2.1 The Defi ne Phase 28 3.2.2 The Assess Phase 31 3.2.3 The Improve Phase 36 3.2.4 The Control Phase (Monitor and Measure) 37 3.3 Conclusions 40 Section II Executing a Data Quality Program 4 Quantification of the Impact of Data Quality 43 4.0 Introduction 43 4.1 Building a Data Quality Cost Quantifi cation Framework 43 4.1.1 The Cost Waterfall 44 4.1.2 Prioritization Matrix 46 4.1.3 Remediation and Return on Investment 50 4.2 A Trading Offi ce Illustrative Example 51 4.3 Conclusions 54 5 Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting 55 5.0 Introduction 55 5.1 What Is Statistical Process Control? 55 5.1.1 Common Causes and Special Causes 57 5.2 Control Charts 59 5.2.1 Different Types of Data 59 5.2.2 Sample and Sample Parameters 60 5.2.3 Construction of Attribute Control Charts 62 5.2.4 Construction of Variable Control Charts 65 5.2.5 Other Control Charts 67 5.2.6 Multivariate Process Control Charts 69 5.3 Relevance of Statistical Process Control in Data Quality Monitoring and Reporting 69 5.4 Conclusions 70 6 Critical Data Elements: Identification, Validation, and Assessment 71 6.0 Introduction 71 6.1 Identifi cation of Critical Data Elements 71 6.1.1 Data Elements and Critical Data Elements 71 6.1.2 CDE Rationalization Matrix 72 6.2 Assessment of Critical Data Elements 75 6.2.1 Data Quality Dimensions 76 6.2.2 Data Quality Business Rules 78 6.2.3 Data Profi ling 79 6.2.4 Measurement of Data Quality Scores 80 6.2.5 Results Recording and Reporting (Scorecard) 80 6.3 Conclusions 82 7 Prioritization of Critical Data Elements (Funnel Approach) 83 7.0 Introduction 83 7.1 The Funnel Methodology (Statistical Analysis for CDE Reduction) 83 7.1.1 Correlation and Regression Analysis for Continuous CDEs 85 7.1.2 Association Analysis for Discrete CDEs 88 7.1.3 Signal-to-Noise Ratios Analysis 90 7.2 Case Study: Basel II 91 7.2.1 Basel II: CDE Rationalization Matrix 91 7.2.2 Basel II: Correlation and Regression Analysis 94 7.2.3 Basel II: Signal-to-Noise (S/N) Ratios 96 7.3 Conclusions 99 8 Data Quality Monitoring and Reporting Scorecards 101 8.0 Introduction 101 8.1 Development of the DQ Scorecards 102 8.2 Analytical Framework (ANOVA, SPCs, Thresholds, Heat Maps) 102 8.2.1 Thresholds and Heat Maps 103 8.2.2 Analysis of Variance (ANOVA) and SPC Charts 107 8.3 Application of the Framework 109 8.4 Conclusions 112 9 Data Quality Issue Resolution 113 9.0 Introduction 113 9.1 Description of the Methodology 113 9.2 Data Quality Methodology 114 9.3 Process Quality/Six Sigma Approach 115 9.4 Case Study: Issue Resolution Process Reengineering 117 9.5 Conclusions 119 10 Information System Testing 121 10.0 Introduction 121 10.1 Typical System Arrangement 122 10.1.1 The Role of Orthogonal Arrays 123 10.2 Method of System Testing 123 10.2.1 Study of Two-Factor Combinations 123 10.2.2 Construction of Combination Tables 124 10.3 MTS Software Testing 126 10.4 Case Study: A Japanese Software Company 130 10.5 Case Study: A Finance Company 133 10.6 Conclusions 138 11 Statistical Approach for Data Tracing 139 11.0 Introduction 139 11.1 Data Tracing Methodology 139 11.1.1 Statistical Sampling 142 11.2 Case Study: Tracing 144 11.2.1 Analysis of Test Cases and CDE Prioritization 144 11.3 Data Lineage through Data Tracing 149 11.4 Conclusions 151 12 Design and Development of Multivariate Diagnostic Systems 153 12.0 Introduction 153 12.1 The Mahalanobis-Taguchi Strategy 153 12.1.1 The Gram Schmidt Orthogonalization Process 155 12.2 Stages in MTS 158 12.3 The Role of Orthogonal Arrays and Signal-to-Noise Ratio in Multivariate Diagnosis 159 12.3.1 The Role of Orthogonal Arrays 159 12.3.2 The Role of S/N Ratios in MTS 161 12.3.3 Types of S/N Ratios 162 12.3.4 Direction of Abnormals 164 12.4 A Medical Diagnosis Example 172 12.5 Case Study: Improving Client Experience 175 12.5.1 Improvements Made Based on Recommendations from MTS Analysis 177 12.6 Case Study: Understanding the Behavior Patterns of Defaulting Customers 178 12.7 Case Study: Marketing 180 12.7.1 Construction of the Reference Group 181 12.7.2 Validation of the Scale 181 12.7.3 Identification of Useful Variables 181 12.8 Case Study: Gear Motor Assembly 182 12.8.1 Apparatus 183 12.8.2 Sensors 184 12.8.3 High-Resolution Encoder 184 12.8.4 Life Test 185 12.8.5 Characterization 185 12.8.6 Construction of the Reference Group or Mahalanobis Space 186 12.8.7 Validation of the MTS Scale 187 12.8.8 Selection of Useful Variables 188 12.9 Conclusions 189 13 Data Analytics 191 13.0 Introduction 191 13.1 Data and Analytics as Key Resources 191 13.1.1 Different Types of Analytics 193 13.1.2 Requirements for Executing Analytics 195 13.1.3 Process of Executing Analytics 196 13.2 Data Innovation 197 13.2.1 Big Data 198 13.2.2 Big Data Analytics 199 13.2.3 Big Data Analytics Operating Model 206 13.2.4 Big Data Analytics Projects: Examples 207 13.3 Conclusions 208 14. Building a Data Quality Practices Center 209 14.0 Introduction 209 14.1 Building a DQPC 209 14.2 Conclusions 211 Appendix A 213 Equations for Signal-to-Noise (S/N) Ratios 213 Nondynamic S/N Ratios 213 Dynamic S/N Ratios 214 Appendix B 217 Matrix Theory: Related Topics 217 What Is a Matrix? 217 Appendix C 221 Some Useful Orthogonal Arrays 221 Two-Level Orthogonal Arrays 221 Three-Level Orthogonal Arrays 255 Index of Terms and Symbols 259 References 261 Index 267
Foreword xiii Prelude xv Preface xvii Acknowledgments xix 1 The Importance of Data Quality 1 1.0 Introduction 1 1.1 Understanding the Implications of Data Quality 1 1.2 The Data Management Function 4 1.3 The Solution Strategy 6 1.4 Guide to This Book 6 Section I Building a Data Quality Program 2 The Data Quality Operating Model 13 2.0 Introduction 13 2.1 Data Quality Foundational Capabilities 13 2.1.1 Program Strategy and Governance 14 2.1.2 Skilled Data Quality Resources 14 2.1.3 Technology Infrastructure and Metadata 15 2.1.4 Data Profi ling and Analytics 15 2.1.5 Data Integration 15 2.1.6 Data Assessment 16 2.1.7 Issues Resolution (IR) 16 2.1.8 Data Quality Monitoring and Control 16 2.2 The Data Quality Methodology 17 2.2.1 Establish a Data Quality Program 17 2.2.2 Conduct a Current-State Analysis 17 2.2.3 Strengthen Data Quality Capability through Data Quality Projects 18 2.2.4 Monitor the Ongoing Production Environment and Measure Data Quality Improvement Effectiveness 18 2.2.5 Detailed Discussion on Establishing the Data Quality Program 18 2.2.6 Assess the Current State of Data Quality 21 2.3 Conclusions 22 3 The DAIC Approach 23 3.0 Introduction 23 3.1 Six Sigma Methodologies 23 3.1.1 Development of Six Sigma Methodologies 25 3.2 DAIC Approach for Data Quality 28 3.2.1 The Defi ne Phase 28 3.2.2 The Assess Phase 31 3.2.3 The Improve Phase 36 3.2.4 The Control Phase (Monitor and Measure) 37 3.3 Conclusions 40 Section II Executing a Data Quality Program 4 Quantification of the Impact of Data Quality 43 4.0 Introduction 43 4.1 Building a Data Quality Cost Quantifi cation Framework 43 4.1.1 The Cost Waterfall 44 4.1.2 Prioritization Matrix 46 4.1.3 Remediation and Return on Investment 50 4.2 A Trading Offi ce Illustrative Example 51 4.3 Conclusions 54 5 Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting 55 5.0 Introduction 55 5.1 What Is Statistical Process Control? 55 5.1.1 Common Causes and Special Causes 57 5.2 Control Charts 59 5.2.1 Different Types of Data 59 5.2.2 Sample and Sample Parameters 60 5.2.3 Construction of Attribute Control Charts 62 5.2.4 Construction of Variable Control Charts 65 5.2.5 Other Control Charts 67 5.2.6 Multivariate Process Control Charts 69 5.3 Relevance of Statistical Process Control in Data Quality Monitoring and Reporting 69 5.4 Conclusions 70 6 Critical Data Elements: Identification, Validation, and Assessment 71 6.0 Introduction 71 6.1 Identifi cation of Critical Data Elements 71 6.1.1 Data Elements and Critical Data Elements 71 6.1.2 CDE Rationalization Matrix 72 6.2 Assessment of Critical Data Elements 75 6.2.1 Data Quality Dimensions 76 6.2.2 Data Quality Business Rules 78 6.2.3 Data Profi ling 79 6.2.4 Measurement of Data Quality Scores 80 6.2.5 Results Recording and Reporting (Scorecard) 80 6.3 Conclusions 82 7 Prioritization of Critical Data Elements (Funnel Approach) 83 7.0 Introduction 83 7.1 The Funnel Methodology (Statistical Analysis for CDE Reduction) 83 7.1.1 Correlation and Regression Analysis for Continuous CDEs 85 7.1.2 Association Analysis for Discrete CDEs 88 7.1.3 Signal-to-Noise Ratios Analysis 90 7.2 Case Study: Basel II 91 7.2.1 Basel II: CDE Rationalization Matrix 91 7.2.2 Basel II: Correlation and Regression Analysis 94 7.2.3 Basel II: Signal-to-Noise (S/N) Ratios 96 7.3 Conclusions 99 8 Data Quality Monitoring and Reporting Scorecards 101 8.0 Introduction 101 8.1 Development of the DQ Scorecards 102 8.2 Analytical Framework (ANOVA, SPCs, Thresholds, Heat Maps) 102 8.2.1 Thresholds and Heat Maps 103 8.2.2 Analysis of Variance (ANOVA) and SPC Charts 107 8.3 Application of the Framework 109 8.4 Conclusions 112 9 Data Quality Issue Resolution 113 9.0 Introduction 113 9.1 Description of the Methodology 113 9.2 Data Quality Methodology 114 9.3 Process Quality/Six Sigma Approach 115 9.4 Case Study: Issue Resolution Process Reengineering 117 9.5 Conclusions 119 10 Information System Testing 121 10.0 Introduction 121 10.1 Typical System Arrangement 122 10.1.1 The Role of Orthogonal Arrays 123 10.2 Method of System Testing 123 10.2.1 Study of Two-Factor Combinations 123 10.2.2 Construction of Combination Tables 124 10.3 MTS Software Testing 126 10.4 Case Study: A Japanese Software Company 130 10.5 Case Study: A Finance Company 133 10.6 Conclusions 138 11 Statistical Approach for Data Tracing 139 11.0 Introduction 139 11.1 Data Tracing Methodology 139 11.1.1 Statistical Sampling 142 11.2 Case Study: Tracing 144 11.2.1 Analysis of Test Cases and CDE Prioritization 144 11.3 Data Lineage through Data Tracing 149 11.4 Conclusions 151 12 Design and Development of Multivariate Diagnostic Systems 153 12.0 Introduction 153 12.1 The Mahalanobis-Taguchi Strategy 153 12.1.1 The Gram Schmidt Orthogonalization Process 155 12.2 Stages in MTS 158 12.3 The Role of Orthogonal Arrays and Signal-to-Noise Ratio in Multivariate Diagnosis 159 12.3.1 The Role of Orthogonal Arrays 159 12.3.2 The Role of S/N Ratios in MTS 161 12.3.3 Types of S/N Ratios 162 12.3.4 Direction of Abnormals 164 12.4 A Medical Diagnosis Example 172 12.5 Case Study: Improving Client Experience 175 12.5.1 Improvements Made Based on Recommendations from MTS Analysis 177 12.6 Case Study: Understanding the Behavior Patterns of Defaulting Customers 178 12.7 Case Study: Marketing 180 12.7.1 Construction of the Reference Group 181 12.7.2 Validation of the Scale 181 12.7.3 Identification of Useful Variables 181 12.8 Case Study: Gear Motor Assembly 182 12.8.1 Apparatus 183 12.8.2 Sensors 184 12.8.3 High-Resolution Encoder 184 12.8.4 Life Test 185 12.8.5 Characterization 185 12.8.6 Construction of the Reference Group or Mahalanobis Space 186 12.8.7 Validation of the MTS Scale 187 12.8.8 Selection of Useful Variables 188 12.9 Conclusions 189 13 Data Analytics 191 13.0 Introduction 191 13.1 Data and Analytics as Key Resources 191 13.1.1 Different Types of Analytics 193 13.1.2 Requirements for Executing Analytics 195 13.1.3 Process of Executing Analytics 196 13.2 Data Innovation 197 13.2.1 Big Data 198 13.2.2 Big Data Analytics 199 13.2.3 Big Data Analytics Operating Model 206 13.2.4 Big Data Analytics Projects: Examples 207 13.3 Conclusions 208 14. Building a Data Quality Practices Center 209 14.0 Introduction 209 14.1 Building a DQPC 209 14.2 Conclusions 211 Appendix A 213 Equations for Signal-to-Noise (S/N) Ratios 213 Nondynamic S/N Ratios 213 Dynamic S/N Ratios 214 Appendix B 217 Matrix Theory: Related Topics 217 What Is a Matrix? 217 Appendix C 221 Some Useful Orthogonal Arrays 221 Two-Level Orthogonal Arrays 221 Three-Level Orthogonal Arrays 255 Index of Terms and Symbols 259 References 261 Index 267
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