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

Includes bibliographical references and index.

1. The importance of data quality--
Section I Building a data quality program:
2. The data quality operating model--
3. The DAIC approach--

Section II Executing a data quality program
4. Quantification of the impact of data quality--
5. Statistical process control and its relevance in data quality monitoring and reporting--
6. Critical data elements: identification, validation, and assessment--
7. Prioritization of critical data elements (funnel approach)--
8. Data quality monitoring and reporting scorecards--
9. Data quality issue resolution--
10. Information system testing--
11. Statistical approach for data tracing--
12. Design and development of multivariate diagnostic systems--
13. Data analytics--
14. Building a data quality practices center--

Appendix A--
Appendix B--
Appendix C--
References--
Index.

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. This book takes a holistic approach to improving data quality, from collection to usage. The author 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.