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Competing with data quality : concepts, tools, and techniques for building a successful approach to data quality /
"Competing with Data Quality provides a road map for corporations to improve data quality and meet Dodd-Frank, BASEL III, Solvency II, and other pervasive regulatory oversight programs. This book outlines a holistic data quality (DQ) approach that businesses can adopt to energize their DQ innov...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- COMPETING WITH HIGH QUALITY DATA; Contents; Foreword; Prelude; Preface; Acknowledgments; Chapter 1 The Importance of Data Quality; 1.0 INTRODUCTION; 1.1 UNDERSTANDING THE IMPLICATIONS OF DATA QUALITY; 1.2 THE DATA MANAGEMENT FUNCTION; 1.3 THE SOLUTION STRATEGY; 1.4 GUIDE TO THIS BOOK; Section I Building a Data Quality Program; Chapter 2 The Data Quality Operating Model; 2.0 INTRODUCTION; 2.1 DATA QUALITY FOUNDATIONAL CAPABILITIES; 2.1.1 Program Strategy and Governance; 2.1.2 Skilled Data Quality Resources; 2.1.3 Technology Infrastructure and Metadata; 2.1.4 Data Profiling and Analytics.
- 2.1.5 Data Integration2.1.6 Data Assessment; 2.1.7 Issues Resolution (IR); 2.1.8 Data Quality Monitoring and Control; 2 .2 THE DATA QUALITY METHODOLOGY; 2.2.1 Establish a Data Quality Program; 2.2.2 Conduct a Current-State Analysis; 2.2.3 Strengthen Data Quality Capability through Data Quality Projects; 2.2.4 Monitor the Ongoing Production Environment and Measure Data Quality Improvement Effectiveness; 2 .2.5 Detailed Discussion on Establishing the Data Quality Program; 2.2.6 Assess the Current State of Data Quality; 2.3 CONCLUSIONS; Chapter 3 The DAIC Approach; 3.0 INTRODUCTION.
- 3.1 SIX SIGMA METHODOLOGIES3.1.1 Development of Six Sigma Methodologies; 3.2 DAIC APPROACH FOR DATA QUALITY; 3.2.1 The Define Phase; 3.2.2 The Assess Phase; 3.2.3 The Improve Phase; 3.2.4 The Control Phase (Monitor and Measure); 3.3 CONCLUSIONS; Section II Executing a Data Quality Program; Chapter 4 Quantification of the Impact of Data Quality; 4.0 INTRODUCTION; 4.1 BUILDING A DATA QUALITY COST QUANTIFICATION FRAMEWORK; 4.1.1 The Cost Waterfall; 4.1.2 Prioritization Matrix; 4.1.3 Remediation and Return on Investment; 4.2 A TRADING OFFICE ILLUSTRATIVE EXAMPLE; 4.3 CONCLUSIONS.
- Chapter 5 Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting5.0 INTRODUCTION; 5.1 WHAT IS STATISTICAL PROCESS CONTROL?; 5.1.1 Common Causes and Special Causes; 5.2 CONTROL CHARTS; 5.2.1 Different Types of Data; 5.2.2 Sample and Sample Parameters; 5.2.3 Construction of Attribute Control Charts; 5.2.4 Construction of Variable Control Charts; 5.2.5 Other Control Charts; 5.2.6 Multivariate Process Control Charts; 5.3 RELEVANCE OF STATISTICAL PROCESS CONTROL IN DATA QUALITY MONITORING AND REPORTING; 5.4 CONCLUSIONS.
- Chapter 6 Critical Data Elements: Identification, Validation, and Assessment6.0 INTRODUCTION; 6.1 IDENTIFICATION OF CRITICAL DATA ELEMENTS; 6.1.1 Data Elements and Critical Data Elements; 6.1.2 CDE Rationalization Matrix; 6.2 ASSESSMENT OF CRITICAL DATA ELEMENTS; 6.2.1 Data Quality Dimensions; 6.2.2 Data Quality Business Rules; 6.2.3 Data Profi ling; 6.2.4 Measurement of Data Quality Scores; 6.2.5 Results Recording and Reporting (Scorecard); 6.3 CONCLUSIONS; Chapter 7 Prioritization of Critical Data Elements (Funnel Approach); 7.0 INTRODUCTION.