Executing data quality projects : ten steps to quality data and trusted information

Executing data quality projects ten steps to quality data and trusted information

Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with...

Descripción completa

Detalles Bibliográficos
Otros Autores: McGilvray, Danette, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Academic Press [2021]
Edición:2nd ed
Materias:
Information technology > Management.
Electronic data processing > Quality control.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835438306719
Tabla de Contenidos:
  • Intro
  • Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM)
  • Copyright
  • In Praise Of
  • Dedication
  • Contents
  • Acknowledgments
  • Foreword
  • Introduction
  • The Reason for This Book
  • What Is in This Book
  • Intended Audiences and How to Use This Book
  • Why a Second Edition
  • My Goals for You
  • Get Started!
  • Structure of This Book
  • Chapter 1 Data Quality and the Data-Dependent World
  • Data, Data Everywhere
  • Trends and the Need for High-Quality Data
  • Data and Information - Assets to Be Managed
  • The Leader's Data Manifesto
  • What You Can Do
  • Are You Ready to Change?
  • Chapter 2 Data Quality in Action
  • Introduction to Chapter 2
  • A Word About Tools
  • Real Issues Need Real Solutions
  • About the Ten Steps Methodology
  • The Data in Action Triangle
  • Preparing Your People
  • Engaging Management
  • Key Terms
  • Chapter 2 Summary
  • Chapter 3 Key Concepts
  • Introduction to Chapter 3
  • The Framework for Information Quality
  • The Information Life Cycle
  • Data Quality Dimensions
  • Business Impact Techniques
  • Data Categories
  • Data Specifications
  • Data Governance and Stewardship
  • Ten Steps Process Overview
  • Data Quality Improvement Cycle
  • Concepts and Action - Making the Connection
  • Chapter 3 Summary
  • Chapter 4 The Ten Steps Process
  • Introduction to Chapter 4
  • Step 1 Determine Business Needs and Approach
  • Introduction to Step 1
  • Step 1.1 Prioritize Business Needs and Select Project Focus
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 1.2 Plan the Project
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 1 Summary
  • Step 2 Analyze Information Environment
  • Introduction to Step 2
  • Step 2.1 Understand Relevant Requirements and Constraints
  • Business Benefit and Context
  • Approach.
  • Sample Output and Templates
  • Step 2.2 Understand Relevant Data and Data Specifications
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 2.3 Understand Relevant Technology
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 2.4 Understand Relevant Processes
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 2.5 Understand Relevant People and Organizations
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 2.6 Understand Relevant Information Life Cycle
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 2 Summary
  • Step 3 Assess Data Quality
  • Introduction to Step 3
  • Step 3.1 Perception of Relevance and Trust
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.2 Data Specifications
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.3 Data Integrity Fundamentals
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.4 Accuracy
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.5 Uniqueness and Deduplication
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.6 Consistency and Synchronization
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.7 Timeliness
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.8 Access
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.9 Security and Privacy
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.10 Presentation Quality
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.11 Data Coverage
  • Business Benefit and Context.
  • Approach
  • Sample Output and Templates
  • Step 3.12 Data Decay
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.13 Usability and Transactability
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3.14 Other Relevant Data Quality Dimensions
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 3 Summary
  • Step 4 Assess Business Impact
  • Introduction to Step 4
  • Step 4.1 Anecdotes
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.2 Connect the Dots
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.3 Usage
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.4 Five Whys for Business Impact
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.5 Process Impact
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.6 Risk Analysis
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.7 Perception of Relevance and Trust
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.8 Benefit vs. Cost Matrix
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.9 Ranking and Prioritization
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.10 Cost of Low-Quality Data
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.11 Cost-Benefit Analysis and ROI
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4.12 Other Relevant Business Impact Techniques
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 4 Summary
  • Step 5 Identify Root Causes
  • Introduction to Step 5.
  • Step 5.1 Five Whys for Root Causes
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 5.2 Track and Trace
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 5.3 Cause-and-Effect/Fishbone Diagram
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 5.4 Other Relevant Root Cause Analysis Techniques
  • Business Benefit and Context
  • Step 5 Summary
  • Step 6 Develop Improvement Plans
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 6 Summary
  • Step 7 Prevent Future Data Errors
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 7 Summary
  • Step 8 Correct Current Data Errors
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 8 Summary
  • Step 9 Monitor Controls
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 9 Summary
  • Step 10 Communicate, Manage, and Engage People Throughout
  • Business Benefit and Context
  • Approach
  • Sample Output and Templates
  • Step 10 Summary
  • Chapter 4 Summary
  • Chapter 5 Structuring Your Project
  • Introduction to Chapter 5
  • Types of Data Quality Projects
  • Project Objectives
  • Comparing SDLCs
  • Data Quality and Governance in SDLCs
  • Roles in Data Quality Projects
  • Project Timing, Communication, and Engagement
  • Chapter 5 Summary
  • Chapter 6 Other Techniques and Tools
  • Introduction to Chapter 6
  • Track Issues and Action Items
  • Design Data Capture and Assessment Plans
  • Analyze, Synthesize, Recommend, Document, and Act on Results
  • Information Life Cycle Approaches
  • Conduct a Survey
  • Metrics
  • The Ten Steps and Other Methodologies and Standards
  • Tools for Managing Data Quality
  • Chapter 6 Summary
  • Chapter 7 A Few Final Words
  • Appendix: Quick References.
  • Framework for Information Quality
  • POSMAD Interaction Matrix Detail
  • Data Quality Dimensions
  • Business Impact Techniques
  • The Ten Steps Process
  • Process Flows for Steps 1-4
  • Data in Action Triangle
  • Glossary
  • List of Figures, Tables, and Templates
  • Bibliography
  • Index
  • About the Author.

Ejemplares similares