Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM)

✍ Scribed by Danette McGilvray

• Publisher: Academic Press
• Year: 2021
• Tongue: English
• Leaves: 378
• Edition: 2
• Category: Library

✦ Synopsis

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 poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations.

The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action.

This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all.

The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.

✦ Table of Contents

Front Cover
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
Back Cover