Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality
Lýsing:
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. It book will also address the end-to-end DQ deployment process with the DQ road map and an approach that is divided into four phases (Define, Assess, Improve and Control).
The focus will be on the application of the DQ methodology to achieve desired levels of data quality in various aspects of an enterprise. DQ methodology is a structured, systematic, and disciplined approach to ensure that we are getting satisfactory levels of data to make correct business decisions by minimizing cost of low quality data. This in turn, strikes a balance between rigor and creativity resulting in reduced DQ assessment and issue resolution times.
Annað
- Höfundur: Rajesh Jugulum
- Útgáfa:1
- Útgáfudagur: 2014-03-10
- Blaðsíður: 304
- Hægt að prenta út 2 bls.
- Hægt að afrita 10 bls.
- Format:ePub
- ISBN 13: 9781118416495
- Print ISBN: 9781118342329
- ISBN 10: 111841649X
Efnisyfirlit
- Cover
- Title
- Copyright
- Dedication
- 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
- 2.2 The Data Quality Methodology
- 2.3 Conclusions
- Note
- Chapter 3: The DAIC Approach
- 3.0 Introduction
- 3.1 Six Sigma Methodologies
- 3.2 DAIC Approach for Data Quality
- 3.3 Conclusions
- Note
- Chapter 2: The Data Quality Operating Model
- Chapter 4: Quantification of the Impact of Data Quality
- 4.0 Introduction
- 4.1 Building a Data Quality Cost Quantification Framework
- 4.2 A Trading Office Illustrative Example
- 4.3 Conclusions
- Note
- 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 Reporting
- 5.4 Conclusions
- Chapter 6: Critical Data Elements: Identification, Validation, and Assessment
- 6.0 Introduction
- 6.1 Identification of Critical Data Elements
- 6.2 Assessment of Critical Data Elements
- 6.3 Conclusions
- Notes
- Chapter 7: Prioritization of Critical Data Elements (Funnel Approach)
- 7.0 Introduction
- 7.1 The Funnel Methodology (Statistical Analysis for CDE Reduction)
- 7.2 Case Study: Basel II
- 7.3 Conclusions
- Notes
- 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)
- 8.3 Application of the Framework
- 8.4 Conclusions
- Note
- Chapter 9: Data Quality Issue Resolution
- 9.0 Introduction
- 9.1 Description of the Methodology
- 9.2 Data Quality Methodology
- 9.3 Process Quality/Six Sigma Approach
- 9.4 Case Study: Issue Resolution Process Reengineering
- 9.5 Conclusions
- Notes
- Chapter 10: Information System Testing
- 10.0 Introduction
- 10.1 Typical System Arrangement
- 10.2 Method of System Testing
- 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
- 11.2 Case Study: Tracing
- 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
- 12.4 A Medical Diagnosis Example
- 12.5 Case Study: Improving Client Experience
- 12.6 Case Study: Understanding the Behavior Patterns of Defaulting Customers
- 12.7 Case Study: Marketing
- 12.8 Case Study: Gear Motor Assembly
- 12.9 Conclusions
- Chapter 13: Data Analytics
- 13.0 Introduction
- 13.1 Data and Analytics as Key Resources
- 13.2 Data Innovation
- 13.3 Conclusions
- Chapter 14: Building a Data Quality Practices Center
- 14.0 Introduction
- 14.1 Building a DQPC
- 14.2 Conclusions
- Equations for Signal-to-Noise (S/N) Ratios
- Matrix Theory: Related Topics
- Some Useful Orthogonal Arrays
- Referenced Resources
- Further Resources
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- Gerð : 208
- Höfundur : 12696
- Útgáfuár : 2014
- Leyfi : 379