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

Read now

Unlock full access

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

Glossary

I like good strong words that mean something.

— Louisa May Alcott, Little Women

So difficult it is to show the various meanings and imperfections of words

when we have nothing else but words to do it with.

— John Locke

Words in italics are defined separately within the glossary.

5G In telecommunications, the fifth-generation technology standard for cellular networks which provides increased download speeds through greater bandwidth speeds and lowered latency.

Access A data quality dimension. The ability to control how authorized users can view, modify, use, or otherwise process data and information.

Accuracy A data quality dimension. The correctness of the content of the data as compared to an agreed-upon and ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9780128180167

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Executing Data Quality Projects, 2nd Edition

by Danette McGilvray

Glossary

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.