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
- Foreword
- Prelude
- Preface
- Acknowledgments
- Chapter 1: The Importance of Data Quality
- Section I: Building a Data Quality Program
-
Section II: Executing a Data Quality Program
- Chapter 4: Quantification of the Impact of Data Quality1
- Chapter 5: Statistical Process Control and Its Relevance in Data Quality Monitoring and Reporting
- Chapter 6: Critical Data Elements: Identification, Validation, and Assessment1
- Chapter 7: Prioritization of Critical Data Elements (Funnel Approach)1
- Chapter 8: Data Quality Monitoring and Reporting Scorecards
- Chapter 9: Data Quality Issue Resolution
- Chapter 10: Information System Testing
- Chapter 11: Statistical Approach for Data Tracing
-
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
- Chapter 14: Building a Data Quality Practices Center
- Appendix A
- Appendix B
- Appendix C
- Index of Terms and Symbols
- References
- Index
- End User License Agreement
Product information
- Title: Competing with High Quality Data: Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality
- Author(s):
- Release date: March 2014
- Publisher(s): Wiley
- ISBN: 9781118342329
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