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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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Jugulum, Rajesh. | |
| 245 | 1 | 0 | |a Competing with data quality : |b concepts, tools, and techniques for building a successful approach to data quality / |c Rajesh Jugulum. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c 2014. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "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 innovation processes, perfect their data gathering and usage practices, and ensure robust and reliable data is available to make judicious decisions. It also addresses the end-to-end DQ deployment process: Define, Assess, Improve and Control. Numerous cases and "lessons learned" facilitate understanding"-- |c Provided by publisher | ||
| 520 | |a "This book aims at outlining a holistic data quality (DQ) approach that businesses can adopt to energize their DQ innovation processes, perfect their data gathering and usage practices and ensure robust and reliable data is available to make judicious decisions"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Electronic data processing |x Quality control. | |
| 650 | 0 | |a Management. | |
| 650 | 6 | |a Gestion. | |
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| 776 | 0 | 8 | |i Print version: |a Jugulum, Rajesh. |t Competing with data quality. |d Hoboken, New Jersey : Wiley, 2014 |z 9781118342329 |w (DLC) 2013038107 |
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