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

Book Description

Create a competitive advantage with data quality

Data is rapidly becoming the powerhouse of industry, but low-quality data can actually put a company at a disadvantage. To be used effectively, data must accurately reflect the real-world scenario it represents, and it must be in a form that is usable and accessible. Quality data involves asking the right questions, targeting the correct parameters, and having an effective internal management, organization, and access system. It must be relevant, complete, and correct, while falling in line with pervasive regulatory oversight programs.

Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality takes a holistic approach to improving data quality, from collection to usage. Author Rajesh Jugulum is globally-recognized as a major voice in the data quality arena, with high-level backgrounds in international corporate finance. In the book, Jugulum provides a roadmap to data quality innovation, covering topics such as:

  • The four-phase approach to data quality control

  • Methodology that produces data sets for different aspects of a business

  • Streamlined data quality assessment and issue resolution

  • A structured, systematic, disciplined approach to effective data gathering

  • The book also contains real-world case studies to illustrate how companies across a broad range of sectors have employed data quality systems, whether or not they succeeded, and what lessons were learned. High-quality data increases value throughout the information supply chain, and the benefits extend to the client, employee, and shareholder. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality provides the information and guidance necessary to formulate and activate an effective data quality plan today.

    Table of Contents

    1. Foreword
    2. Prelude
    3. Preface
    4. Acknowledgments
    5. Chapter 1: The Importance of Data Quality
      1. 1.0 Introduction
      2. 1.1 Understanding the Implications of Data Quality
      3. 1.2 The Data Management Function
      4. 1.3 The Solution Strategy
      5. 1.4 Guide to This Book
    6. Section I: Building a Data Quality Program
      1. Chapter 2: The Data Quality Operating Model<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        1. 2.0 Introduction
        2. 2.1 Data Quality Foundational Capabilities
        3. 2.2 The Data Quality Methodology
        4. 2.3 Conclusions
        5. Note
      2. Chapter 3: The DAIC Approach<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        1. 3.0 Introduction
        2. 3.1 Six Sigma Methodologies
        3. 3.2 DAIC Approach for Data Quality
        4. 3.3 Conclusions
        5. Note
    7. Section II: Executing a Data Quality Program
      1. Chapter 4: Quantification of the Impact of Data Quality<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        1. 4.0 Introduction
        2. 4.1 Building a Data Quality Cost Quantification Framework
        3. 4.2 A Trading Office Illustrative Example
        4. 4.3 Conclusions
        5. Note
      2. Chapter 5: Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting
        1. 5.0 Introduction
        2. 5.1 What Is Statistical Process Control?
        3. 5.2 Control Charts
        4. 5.3 Relevance of Statistical Process Control in Data Quality Monitoring and Reporting
        5. 5.4 Conclusions
      3. Chapter 6: Critical Data Elements: Identification, Validation, and Assessment<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        1. 6.0 Introduction
        2. 6.1 Identification of Critical Data Elements
        3. 6.2 Assessment of Critical Data Elements
        4. 6.3 Conclusions
        5. Notes
      4. Chapter 7: Prioritization of Critical Data Elements (Funnel Approach)<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        1. 7.0 Introduction
        2. 7.1 The Funnel Methodology (Statistical Analysis for CDE Reduction)
        3. 7.2 Case Study: Basel II
        4. 7.3 Conclusions
        5. Notes
      5. Chapter 8: Data Quality Monitoring and Reporting Scorecards
        1. 8.0 Introduction
        2. 8.1 Development of the DQ Scorecards
        3. 8.2 Analytical Framework (ANOVA, SPCs, Thresholds, Heat Maps)
        4. 8.3 Application of the Framework
        5. 8.4 Conclusions
        6. Note
      6. Chapter 9: Data Quality Issue Resolution
        1. 9.0 Introduction
        2. 9.1 Description of the Methodology<sup xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:svg="http://www.w3.org/2000/svg">1</sup>
        3. 9.2 Data Quality Methodology
        4. 9.3 Process Quality/Six Sigma Approach
        5. 9.4 Case Study: Issue Resolution Process Reengineering
        6. 9.5 Conclusions
        7. Notes
      7. Chapter 10: Information System Testing
        1. 10.0 Introduction
        2. 10.1 Typical System Arrangement
        3. 10.2 Method of System Testing
        4. 10.3 MTS Software Testing
        5. 10.4 Case Study: A Japanese Software Company
        6. 10.5 Case Study: A Finance Company
        7. 10.6 Conclusions
      8. Chapter 11: Statistical Approach for Data Tracing
        1. 11.0 Introduction
        2. 11.1 Data Tracing Methodology
        3. 11.2 Case Study: Tracing
        4. 11.3 Data Lineage through Data Tracing
        5. 11.4 Conclusions
      9. Chapter 12: Design and Development of Multivariate Diagnostic Systems
        1. 12.0 Introduction
        2. 12.1 The Mahalanobis-Taguchi Strategy
        3. 12.2 Stages in MTS
        4. 12.3 The Role of Orthogonal Arrays and Signal-to-Noise Ratio in Multivariate Diagnosis
        5. 12.4 A Medical Diagnosis Example
        6. 12.5 Case Study: Improving Client Experience
        7. 12.6 Case Study: Understanding the Behavior Patterns of Defaulting Customers
        8. 12.7 Case Study: Marketing
        9. 12.8 Case Study: Gear Motor Assembly
        10. 12.9 Conclusions
      10. Chapter 13: Data Analytics
        1. 13.0 Introduction
        2. 13.1 Data and Analytics as Key Resources
        3. 13.2 Data Innovation
        4. 13.3 Conclusions
      11. Chapter 14: Building a Data Quality Practices Center
        1. 14.0 Introduction
        2. 14.1 Building a DQPC
        3. 14.2 Conclusions
    8. Appendix A
      1. Equations for Signal-to-Noise (S/N) Ratios
    9. Appendix B
      1. Matrix Theory: Related Topics
    10. Appendix C
      1. Some Useful Orthogonal Arrays
    11. Index of Terms and Symbols
    12. References
      1. Referenced Resources
      2. Further Resources
    13. Index
    14. End User License Agreement