book

Executing Data Quality Projects, 2nd Edition

Name: Executing Data Quality Projects, 2nd Edition
Author: Danette McGilvray
ISBN: 9780128180167

by Danette McGilvray

May 2021

Intermediate to advanced

376 pages

22h 9m

English

Academic Press

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Cover image
Title page
Table of Contents
Copyright
In Praise Of
Dedication
Acknowledgments
Foreword
Introduction
The Reason for This BookWhat Is in This BookIntended Audiences and How to Use This BookWhy a Second EditionMy Goals for YouGet Started!Structure of This Book
Chapter 1: Data Quality and the Data-Dependent World
Data, Data EverywhereTrends and the Need for High-Quality DataData and Information – Assets to Be ManagedThe Leader’s Data ManifestoWhat You Can DoAre You Ready to Change?

Chapter 2: Data Quality in Action
Introduction to Chapter 2A Word About ToolsReal Issues Need Real SolutionsAbout the Ten Steps MethodologyThe Data in Action TrianglePreparing Your PeopleEngaging ManagementKey TermsChapter 2 Summary
Chapter 3: Key Concepts
Introduction to Chapter 3The Framework for Information QualityThe Information Life CycleData Quality DimensionsBusiness Impact TechniquesData CategoriesData SpecificationsData Governance and StewardshipTen Steps Process OverviewData Quality Improvement Cycle
Concepts and Action – Making the Connection
Chapter 3 Summary
Chapter 4: The Ten Steps Process
Introduction to Chapter 4Step 1: Determine Business Needs and ApproachIntroduction to Step 1Step 1.1: Prioritize Business Needs and Select Project FocusBusiness Benefit and ContextApproachSample Output and TemplatesStep 1.2: Plan the ProjectBusiness Benefit and ContextApproachSample Output and TemplatesStep 1 SummaryStep 2: Analyze Information EnvironmentIntroduction to Step 2Step 2.1: Understand Relevant Requirements and ConstraintsBusiness Benefit and ContextApproachSample Output and TemplatesStep 2.2: Understand Relevant Data and Data SpecificationsBusiness Benefit and ContextApproachSample Output and TemplatesStep 2.3: Understand Relevant TechnologyBusiness Benefit and ContextApproachSample Output and TemplatesStep 2.4: Understand Relevant ProcessesBusiness Benefit and ContextApproachSample Output and TemplatesStep 2.5: Understand Relevant People and OrganizationsBusiness Benefit and ContextApproachSample Output and Templates
Step 2.6: Understand Relevant Information Life Cycle
Business Benefit and ContextApproachSample Output and TemplatesStep 2 SummaryStep 3: Assess Data QualityIntroduction to Step 3Step 3.1: Perception of Relevance and TrustBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.2: Data SpecificationsBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.3: Data Integrity FundamentalsBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.4: AccuracyBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.5: Uniqueness and DeduplicationBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.6: Consistency and SynchronizationBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.7: TimelinessBusiness Benefit and ContextApproachSample Output and Templates
Step 3.8: Access
Business Benefit and ContextApproachSample Output and TemplatesStep 3.9: Security and PrivacyBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.10: Presentation QualityBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.11: Data CoverageBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.12: Data DecayBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.13: Usability and TransactabilityBusiness Benefit and ContextApproachSample Output and TemplatesStep 3.14: Other Relevant Data Quality DimensionsBusiness Benefit and ContextApproachSample Output and TemplatesStep 3 SummaryStep 4: Assess Business ImpactIntroduction to Step 4Step 4.1: AnecdotesBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.2: Connect the DotsBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.3: UsageBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.4: Five Whys for Business ImpactBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.5: Process ImpactBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.6: Risk AnalysisBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.7: Perception of Relevance and TrustBusiness Benefit and ContextApproachSample Output and Templates
Step 4.8: Benefit vs. Cost Matrix
Business Benefit and ContextApproachSample Output and TemplatesStep 4.9: Ranking and PrioritizationBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.10: Cost of Low-Quality DataBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.11: Cost-Benefit Analysis and ROIBusiness Benefit and ContextApproachSample Output and TemplatesStep 4.12: Other Relevant Business Impact TechniquesBusiness Benefit and ContextApproachSample Output and TemplatesStep 4 SummaryStep 5: Identify Root CausesIntroduction to Step 5Step 5.1: Five Whys for Root CausesBusiness Benefit and ContextApproachSample Output and TemplatesStep 5.2: Track and TraceBusiness Benefit and ContextApproachSample Output and TemplatesStep 5.3: Cause-and-Effect/Fishbone DiagramBusiness Benefit and ContextApproachSample Output and TemplatesStep 5.4: Other Relevant Root Cause Analysis TechniquesBusiness Benefit and ContextStep 5 SummaryStep 6: Develop Improvement PlansBusiness Benefit and ContextApproachSample Output and TemplatesStep 6 SummaryStep 7: Prevent Future Data ErrorsBusiness Benefit and ContextApproachSample Output and TemplatesStep 7 Summary
Step 8: Correct Current Data Errors
Business Benefit and ContextApproachSample Output and TemplatesStep 8 SummaryStep 9: Monitor ControlsBusiness Benefit and ContextApproachSample Output and TemplatesStep 9 SummaryStep 10: Communicate, Manage, and Engage People ThroughoutBusiness Benefit and ContextApproachSample Output and TemplatesStep 10 SummaryChapter 4 Summary
Chapter 5: Structuring Your Project
Introduction to Chapter 5Types of Data Quality ProjectsProject ObjectivesComparing SDLCsData Quality and Governance in SDLCsRoles in Data Quality ProjectsProject Timing, Communication, and EngagementChapter 5 Summary
Chapter 6: Other Techniques and Tools
Introduction to Chapter 6Track Issues and Action ItemsDesign Data Capture and Assessment PlansAnalyze, Synthesize, Recommend, Document, and Act on ResultsInformation Life Cycle ApproachesConduct a SurveyMetricsThe Ten Steps and Other Methodologies and StandardsTools for Managing Data QualityChapter 6 Summary
Chapter 7: A Few Final Words
Appendix: Quick References
Framework for Information QualityPOSMAD Interaction Matrix DetailData Quality DimensionsBusiness Impact TechniquesThe Ten Steps ProcessProcess Flows for Steps 1-4Data in Action Triangle
Glossary
List of Figures, Tables, and Templates
Bibliography
Index
About the Author

Content preview from Executing Data Quality Projects, 2nd Edition

Index

Note: Page numbers followed by f indicate figures, t indicate tables, and b indicate boxes.

Access 151–153, 151b, 151f, 154f

Access requirements 153, 154f

Access rights matrices 153

Accountabilities 21, 65, 66, 248

Accuracy 135–136, 136f, 139–140

Accuracy assessment 139b

Action items/issue tracking 270–271, 270–271t

Actual data quality 81–82

Acute vs. chronic problems 215b

Ad hoc use See Project types

Aera Energy 173–175, 190

Aggregate data 152

Agile 85, 254, 257, 259–260, 264t, 267

Agnew, P. 62–63t

AI See Artificial Intelligence (AI)

Algmin, A.J. 299, 304b

Allemang, D. 62–63t

Altarade, M. 104

Analyze 274, 276–277

Analyze information environment 92–96, 92f, 93–94t, 117–118, 315, 316f

Anecdotes 172–176,

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Executing Data Quality Projects, 2nd Edition

by Danette McGilvray

Index

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