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Informationen zum Autor Laura Sebastian-Coleman, Data Quality Director at Prudential, has been a data quality practitioner since 2003. She has implemented data quality metrics and reporting, launched and facilitated working stewardship groups, contributed to data consumer training programs, and led efforts to establish data standards and manage metadata. In 2009, she led a group of analysts in developing the Data Quality Assessment Framework (DQAF), which is the basis for her 2013 book, Measuring Data Quality for Ongoing Improvement. An active professional, Laura has delivered papers, tutorials, and keynotes at data-focused conferences, such as MIT’s Information Quality Program, Data Governance and Information Quality (DGIQ), Enterprise Data World (EDW), Data Modeling Zone, and Data Management Association (DAMA)-sponsored events. From 2009 to 2010, she served as IAIDQ’s Director of Member Services. In 2015, she received the IAIDQ Distinguished Member Award. DAMA Publications Officer (2015 to 2018) and production editor for the DAMA-DMBOK2 (2017), she is also author of Navigating the Labyrinth: An Executive Guide to Data Management (2018). In 2018, she received the DAMA award for excellence in the data management profession. She holds a CDMP (Certified Data Management Professional) from DAMA, an IQCP (Information Quality Certified Professional) from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and a Ph.D. in English Literature from the University of Rochester. Klappentext The only book that explicitly teaches how to measure the quality of data over time. "If you are intent on improving the quality of the data at your organization you would do well to read Measuring Data Quality for Ongoing Improvement and adopt the DQAF offered up in this fine book."--Data and Technology Today blog, July 2, 2013 Inhaltsverzeichnis Section One: Concepts and Definitions Chapter 1: Data Chapter 2: Data, People, and Systems Chapter 3: Data Management, Models, and Metadata Chapter 4: Data Quality and Measurement Section Two: DQAF Concepts and Measurement Types Chapter 5: DQAF Concepts Chapter 6: DQAF Measurement Types Section Three: Data Assessment Scenarios Chapter 7: Initial Data Assessment Chapter 8 Assessment in Data Quality Improvement Projects Chapter 9: Ongoing Measurement Section Four: Applying the DQAF to Data Requirements Chapter 10: Requirements, Risk, Criticality Chapter 11: Asking Questions Section Five: A Strategic Approach to Data Quality Chapter 12: Data Quality Strategy Chapter 13: Quality Improvement and Data Quality Chapter 14: Directives for Data Quality Strategy Section Six: The DQAF in Depth Chapter 15: Functions of Measurement: Collection, Calculation, Comparison Chapter 16: Features of the DQAF Measurement Logical Chapter 17: Facets of the DQAF Measurement Types Appendix A: Measuring the Value of Data Appendix B: Data Quality Dimensions Appendix C: Completeness, Consistency, and Integrity of the Data Model Appendix D: Prediction, Error, and Shewhart's lost disciple, Kristo Ivanov Glossary Bibliography ...
