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.

"This book provides a very well-structured introduction to the fundamental issue of data quality, making it a very useful tool for managers, practitioners, analysts, software developers, and systems engineers. It also helps explain what data quality management entails and provides practical approaches aimed at actual implementation. I positively recommend reading it…" --ComputingReviews.com, January 2014"The framework she describes is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. The material is for people who are charged with improving, monitoring, or ensuring data quality." --Reference and Research Book News, August 2013"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 2013

• Section One: Concepts and DefinitionsChapter 1: DataChapter 2: Data, People, and Systems Chapter 3: Data Management, Models, and MetadataChapter 4: Data Quality and MeasurementSection Two: DQAF Concepts and Measurement TypesChapter 5: DQAF ConceptsChapter 6: DQAF Measurement TypesSection Three: Data Assessment ScenariosChapter 7: Initial Data AssessmentChapter 8 Assessment in Data Quality Improvement ProjectsChapter 9: Ongoing Measurement Section Four: Applying the DQAF to Data RequirementsChapter 10: Requirements, Risk, CriticalityChapter 11: Asking QuestionsSection Five: A Strategic Approach to Data QualityChapter 12: Data Quality StrategyChapter 13: Quality Improvement and Data QualityChapter 14: Directives for Data Quality Strategy Section Six: The DQAF in DepthChapter 15: Functions of Measurement: Collection, Calculation, ComparisonChapter 16: Features of the DQAF Measurement Logical Chapter 17: Facets of the DQAF Measurement TypesAppendix A: Measuring the Value of DataAppendix B: Data Quality DimensionsAppendix C: Completeness, Consistency, and Integrity of the Data ModelAppendix D: Prediction, Error, and Shewhart’s lost disciple, Kristo IvanovGlossaryBibliography

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.

"This book provides a very well-structured introduction to the fundamental issue of data quality, making it a very useful tool for managers, practitioners, analysts, software developers, and systems engineers. It also helps explain what data quality management entails and provides practical approaches aimed at actual implementation. I positively recommend reading it…" --ComputingReviews.com, January 2014"The framework she describes is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. The material is for people who are charged with improving, monitoring, or ensuring data quality." --Reference and Research Book News, August 2013"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 2013

• Section One: Concepts and DefinitionsChapter 1: DataChapter 2: Data, People, and Systems Chapter 3: Data Management, Models, and MetadataChapter 4: Data Quality and MeasurementSection Two: DQAF Concepts and Measurement TypesChapter 5: DQAF ConceptsChapter 6: DQAF Measurement TypesSection Three: Data Assessment ScenariosChapter 7: Initial Data AssessmentChapter 8 Assessment in Data Quality Improvement ProjectsChapter 9: Ongoing Measurement Section Four: Applying the DQAF to Data RequirementsChapter 10: Requirements, Risk, CriticalityChapter 11: Asking QuestionsSection Five: A Strategic Approach to Data QualityChapter 12: Data Quality StrategyChapter 13: Quality Improvement and Data QualityChapter 14: Directives for Data Quality Strategy Section Six: The DQAF in DepthChapter 15: Functions of Measurement: Collection, Calculation, ComparisonChapter 16: Features of the DQAF Measurement Logical Chapter 17: Facets of the DQAF Measurement TypesAppendix A: Measuring the Value of DataAppendix B: Data Quality DimensionsAppendix C: Completeness, Consistency, and Integrity of the Data ModelAppendix D: Prediction, Error, and Shewhart’s lost disciple, Kristo IvanovGlossaryBibliography

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.

"This book provides a very well-structured introduction to the fundamental issue of data quality, making it a very useful tool for managers, practitioners, analysts, software developers, and systems engineers. It also helps explain what data quality management entails and provides practical approaches aimed at actual implementation. I positively recommend reading it…" --ComputingReviews.com, January 2014"The framework she describes is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. The material is for people who are charged with improving, monitoring, or ensuring data quality." --Reference and Research Book News, August 2013"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 2013

• Section One: Concepts and DefinitionsChapter 1: DataChapter 2: Data, People, and Systems Chapter 3: Data Management, Models, and MetadataChapter 4: Data Quality and MeasurementSection Two: DQAF Concepts and Measurement TypesChapter 5: DQAF ConceptsChapter 6: DQAF Measurement TypesSection Three: Data Assessment ScenariosChapter 7: Initial Data AssessmentChapter 8 Assessment in Data Quality Improvement ProjectsChapter 9: Ongoing Measurement Section Four: Applying the DQAF to Data RequirementsChapter 10: Requirements, Risk, CriticalityChapter 11: Asking QuestionsSection Five: A Strategic Approach to Data QualityChapter 12: Data Quality StrategyChapter 13: Quality Improvement and Data QualityChapter 14: Directives for Data Quality Strategy Section Six: The DQAF in DepthChapter 15: Functions of Measurement: Collection, Calculation, ComparisonChapter 16: Features of the DQAF Measurement Logical Chapter 17: Facets of the DQAF Measurement TypesAppendix A: Measuring the Value of DataAppendix B: Data Quality DimensionsAppendix C: Completeness, Consistency, and Integrity of the Data ModelAppendix D: Prediction, Error, and Shewhart’s lost disciple, Kristo IvanovGlossaryBibliography