Competing with high quality data : concepts, tools, and techniques for building a successful approach to data quality / Rajesh Jugulum ; cover design, C. Wallace.

Format:

Book

Author/Creator:

Jugulum, Rajesh.

Contributor:

Wallace, C.

Language:

English

Subjects (All):

Electronic data processing--Quality control.

Electronic data processing.

Physical Description:

xx, 277 pages.

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2014.

System Details:

text file

Summary:

"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"-- Provided by publisher.

Contents:

Intro

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 Integration

2.1.6 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 METHODOLOGIES

3.1.1 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 Reporting

5.0 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 Assessment

6.0 I NTRODUCTION

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

7.1 THE FUNNEL METHODOLOGY (STATISTICAL ANALYSIS FOR CDE REDUCTION)

7.1.1 Correlation and Regression Analysis for Continuous CDEs

7.1.2 Association Analysis for Discrete CDEs

7.1.3 Signal-to-Noise Ratios Analysis

7.2 CASE STUDY: BASEL II

7.2.1 Basel II: CDE Rationalization Matrix

7.2.2 Basel II: Correlation and Regression Analysis

7.2.3 Basel II: Signal-to-Noise (S/N) Ratios

7.3 CONCLUSIONS

Chapter 8 Data Quality Monitoring and Reporting Scorecards

8.0 INTRODUCTION

8.1 DEVELOPMENT OF THE DQ SCORECARDS

8.2 ANALYTICAL FRAMEWORK (ANOVA, SPCs, THRESHOLDS, HEAT MAPS)

8.2.1 Thresholds and Heat Maps

8.2.2 Analysis of Variance (ANOVA) and SPC Charts.

8.3 APPLICATION OF THE FRAMEWORK

8.4 CONCLUSIONS

Chapter 9 Data Quality Issue Resolution

9.0 INTRODUCTION

9.1 DESCRIPTION OF THE METHODOLOGY1

9.2 DATA QUALITY METHODOLOGY

9.3 PROCESS QUALITY/SIX SIGMA APPROACH

9.4 CASE STUDY: ISSUE RESOLUTION PROCESS REENGINEERING

9.5 CONCLUSIONS

Chapter 10 Information System Testing

10.0 INTRODUCTION

10.1 TYPICAL SYSTEM ARRANGEMENT

10.1.1 The Role of Orthogonal Arrays

10.2 METHOD OF SYSTEM TESTING

10.2.1 Study of Two-Factor Combinations

10.2.2 Construction of Combination Tables

10.3 MTS SOFTWARE TESTING

10.4 CASE STUDY: A JAPANESE SOFTWARE COMPANY

10.5 CASE STUDY: A FINANCE COMPANY

10.6 CONCLUSIONS

Chapter 11 Statistical Approach for Data Tracing

11.0 INTRODUCTION

11.1 DATA TRACING METHODOLOGY

11.1.1 Statistical Sampling

11.2 CASE STUDY: TRACING

11.2.1 Analysis of Test Cases and CDE Prioritization

11.3 DATA LINEAGE THROUGH DATA TRACING

11.4 CONCLUSIONS

Chapter 12 Design and Development of Multivariate Diagnostic Systems

12.0 INTRODUCTION

12.1 THE MAHALANOBIS-TAGUCHI STRATEGY

12.2 STAGES IN MTS

12.3 THE ROLE OF ORTHOGONAL ARRAYS AND SIGNAL-TO-NOISE RATIO IN MULTIVARIATE DIAGNOSIS

12.3.1 The Role of Orthogonal Arrays

12.3.2 The Role of S/N Ratios in MTS

12.3.3 Types of S/N Ratios

12.3.4 Direction of Abnormals

12.4 A MEDICAL DIAGNOSIS EXAMPLE

12.5 CASE STUDY: IMPROVING CLIENT EXPERIENCE

12.5.1 Improvements Made Based on Recommendations from MTS Analysis

12.6 CASE STUDY: UNDERSTANDING THE BEHAVIOR PATTERNS OF DEFAULTING CUSTOMERS

12.7 CASE STUDY: MARKETING

12.7.1 Construction of the Reference Group

12.7.2 Validation of the Scale

12.7.3 Identification of Useful Variables

12.8 CASE STUDY: GEAR MOTOR ASSEMBLY

12.8.1 Apparatus

12.8.2 Sensors

12.8.3 High-Resolution Encoder.

12.8.4 Life Test

12.8.5 Characterization

12.8.6 Construction of the Reference Group or Mahalanobis Space

12.8.7 Validation of the MTS Scale

12.8.8 Selection of Useful Variables

12.9 CONCLUSIONS

Chapter 13 Data Analytics

13.0 INTRODUCTION

13.1 DATA AND ANALYTICS AS KEY RESOURCES

13.1.1 Different Types of Analytics

13.1.2 Requirements for Executing Analytics

13.1.3 Process of Executing Analytics

13.2 DATA INNOVATION

13.2.1 Big Data

13.2.2 Big Data Analytics

13.2.3 Big Data Analytics Operating Model

13.2.4 Big Data Analytics Projects: Examples

13.3 CONCLUSIONS

Chapter 14 Building a Data Quality Practices Center

14.0 INTRODUCTION

14.1 BUILDING A DQPC

14.2 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

Index.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed March 10, 2014).

ISBN:

9781118416495

111841649X

9781118840962

1118840968

9781118420133

1118420136

OCLC:

864418253