book

Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality

Name: Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality
Author: Rajesh Jugulum
ISBN: 9781118342329

by Rajesh Jugulum

March 2014

Intermediate to advanced

304 pages

6h 6m

English

Wiley

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Foreword
Prelude
Preface
Acknowledgments
Chapter 1: The Importance of Data Quality
1.0 Introduction 1.1 Understanding the Implications of Data Quality1.2 The Data Management Function1.3 The Solution Strategy1.4 Guide to This Book
Section I: Building a Data Quality Program
Chapter 2: The Data Quality Operating Model1
2.0 Introduction 2.1 Data Quality Foundational Capabilities 2.2 The Data Quality Methodology 2.3 ConclusionsNote
Chapter 3: The DAIC Approach1
3.0 Introduction3.1 Six Sigma Methodologies3.2 DAIC Approach for Data Quality3.3 ConclusionsNote
Section II: Executing a Data Quality Program
Chapter 4: Quantification of the Impact of Data Quality1
4.0 Introduction4.1 Building a Data Quality Cost Quantification Framework4.2 A Trading Office Illustrative Example4.3 ConclusionsNote

Chapter 5: Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting
5.0 Introduction 5.1 What Is Statistical Process Control?5.2 Control Charts 5.3 Relevance of Statistical Process Control in Data Quality Monitoring and Reporting5.4 Conclusions
Chapter 6: Critical Data Elements: Identification, Validation, and Assessment1
6.0 Introduction6.1 Identification of Critical Data Elements6.2 Assessment of Critical Data Elements6.3 ConclusionsNotes
Chapter 7: Prioritization of Critical Data Elements (Funnel Approach)1
7.0 Introduction7.1 The Funnel Methodology (Statistical Analysis for CDE Reduction)7.2 Case Study: Basel II 7.3 ConclusionsNotes
Chapter 8: Data Quality Monitoring and Reporting Scorecards
8.0 Introduction8.1 Development of the DQ Scorecards8.2 Analytical Framework (ANOVA, SPCs, Thresholds, Heat Maps)8.3 Application of the Framework8.4 ConclusionsNote
Chapter 9: Data Quality Issue Resolution
9.0 Introduction9.1 Description of the Methodology19.2 Data Quality Methodology9.3 Process Quality/Six Sigma Approach9.4 Case Study: Issue Resolution Process Reengineering9.5 Conclusions Notes
Chapter 10: Information System Testing
10.0 Introduction10.1 Typical System Arrangement10.2 Method of System Testing10.3 MTS Software Testing 10.4 Case Study: A Japanese Software Company10.5 Case Study: A Finance Company10.6 Conclusions
Chapter 11: Statistical Approach for Data Tracing
11.0 Introduction11.1 Data Tracing Methodology11.2 Case Study: Tracing11.3 Data Lineage through Data Tracing11.4 Conclusions
Chapter 12: Design and Development of Multivariate Diagnostic Systems
12.0 Introduction12.1 The Mahalanobis-Taguchi Strategy 12.2 Stages in MTS12.3 The Role of Orthogonal Arrays and Signal-to-Noise Ratio in Multivariate Diagnosis12.4 A Medical Diagnosis Example12.5 Case Study: Improving Client Experience 12.6 Case Study: Understanding the Behavior Patterns of Defaulting Customers12.7 Case Study: Marketing12.8 Case Study: Gear Motor Assembly12.9 Conclusions
Chapter 13: Data Analytics
13.0 Introduction13.1 Data and Analytics as Key Resources13.2 Data Innovation13.3 Conclusions
Chapter 14: Building a Data Quality Practices Center
14.0 Introduction14.1 Building a DQPC14.2 Conclusions
Appendix A
Equations for Signal-to-Noise (S/N) Ratios
Appendix B
Matrix Theory: Related Topics
Appendix C
Some Useful Orthogonal Arrays
Index of Terms and Symbols
References
Referenced ResourcesFurther Resources
Index
End User License Agreement

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Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality

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