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.
1. Introduction to Chapter 4 - p. 74 2. Step 1. Determine Business Needs and Approach - p. 78 3. Step 1.1. Prioritize Business Needs and Select Project Focus - p. 80 4. Step 1.2. Plan the Project - p. 83 5. Step 2. Analyze Information Environment - p. 92 6. Step 2.1. Understand Relevant Requirements and Constraints - p. 96 7. Step 2.2. Understand Relevant Data and Data Specifications - p. 99 8. Step 2.3. Understand Relevant Technology - p. 103 9. Step 2.4. Understand Relevant Processes - p. 107 10. Step 2.5. Understand Relevant People and Organizations - p. 110 11. Step 2.6. Understand Relevant Information Life Cycle - p. 113 12. Step 3. Assess Data Quality - p. 119 13. Step 3.1. Perception of Relevance and Trust - p. 123 14. Step 3.2. Data Specifications - p. 123 15. Step 3.3. Data Integrity Fundamentals - p. 128 16. Step 3.4. Accuracy - p. 135 17. Business Benefit and Context - p. 135 18. Approach - p. 136 19. Sample Output and Templates - p. 139 20. Step 3.5. Uniqueness and Deduplication - p. 140 21. Step 3.6. Consistency and Synchronization - p. 146 22. Step 3.7. Timeliness - p. 148 23. Step 3.8. Access - p. 151 24. Step 3.9. Security and Privacy - p. 155 25. Step 3.10. Presentation Quality - p. 159 26. Step 3.11. Data Coverage - p. 161 27. Step 3.12. Data Decay - p. 163 28. Step 3.13. Usability and Transactability - p. 165 29. Step 3.14. Other Relevant Data Quality Dimensions - p. 166 30. Step 4. Assess Business Impact - p. 168 31. Step 4.1. Anecdotes - p. 172 32. Step 4.2. Connect the Dots - p. 176 33. Step 4.3. Usage - p. 179 34. Step 4.4. Five Whys for Business Impact - p. 181 35. Step 4.5. Process Impact - p. 182 36. Step 4.6. Risk Analysis - p. 185 37. Step 4.7. Perception of Relevance and Trust - p. 187 38. Business Benefit and Context - p. 187 39. Approach - p. 188 40. Sample Output and Templates - p. 189 41. Step 4.8. Benefit vs. Cost Matrix - p. 191 42. Step 4.9. Ranking and Prioritization - p. 195 43. Step 4.10. Cost of Low-Quality Data - p. 198 44. Step 4.11. Cost-Benefit Analysis and ROI - p. 202 45. Step 4.12. Other Relevant Business Impact Techniques - p. 204 46. Step 5. Identify Root Causes - p. 206 47. Step 5.1. Five Whys for Root Causes - p. 210 48. Step 5.2. Track and Trace - p. 212 49. Step 5.3. Cause-and-Effect/Fishbone Diagram - p. 213 50. Step 5.4. Other Relevant Root Cause Analysis Techniques - p. 218 51. Step 6. Develop Improvement Plans - p. 220 52. Step 7. Prevent Future Data Errors - p. 227 53. Step 8. Correct Current Data Errors - p. 232 54. Step 9. Monitor Controls - p. 236 55. Communicate, Manage, and Engage People Throughout - p. 242 56. Chapter 4 Summary - p. 252 57. Chapter 5. Structuring Your Project - p. 261 58. Chapter 6. Other Techniques and Tools - p. 272 59. Chapter 7. A Few Final Words - p. 305 60. Appendix: Quick References - p. 307 61. Glossary - p. 321 62. List of Figures, Tables, and Templates - p. 331 63. Bibliography - p. 339 64. Index - p. 347 65. About the Author - p. 355
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.
1. Introduction to Chapter 4 - p. 74 2. Step 1. Determine Business Needs and Approach - p. 78 3. Step 1.1. Prioritize Business Needs and Select Project Focus - p. 80 4. Step 1.2. Plan the Project - p. 83 5. Step 2. Analyze Information Environment - p. 92 6. Step 2.1. Understand Relevant Requirements and Constraints - p. 96 7. Step 2.2. Understand Relevant Data and Data Specifications - p. 99 8. Step 2.3. Understand Relevant Technology - p. 103 9. Step 2.4. Understand Relevant Processes - p. 107 10. Step 2.5. Understand Relevant People and Organizations - p. 110 11. Step 2.6. Understand Relevant Information Life Cycle - p. 113 12. Step 3. Assess Data Quality - p. 119 13. Step 3.1. Perception of Relevance and Trust - p. 123 14. Step 3.2. Data Specifications - p. 123 15. Step 3.3. Data Integrity Fundamentals - p. 128 16. Step 3.4. Accuracy - p. 135 17. Business Benefit and Context - p. 135 18. Approach - p. 136 19. Sample Output and Templates - p. 139 20. Step 3.5. Uniqueness and Deduplication - p. 140 21. Step 3.6. Consistency and Synchronization - p. 146 22. Step 3.7. Timeliness - p. 148 23. Step 3.8. Access - p. 151 24. Step 3.9. Security and Privacy - p. 155 25. Step 3.10. Presentation Quality - p. 159 26. Step 3.11. Data Coverage - p. 161 27. Step 3.12. Data Decay - p. 163 28. Step 3.13. Usability and Transactability - p. 165 29. Step 3.14. Other Relevant Data Quality Dimensions - p. 166 30. Step 4. Assess Business Impact - p. 168 31. Step 4.1. Anecdotes - p. 172 32. Step 4.2. Connect the Dots - p. 176 33. Step 4.3. Usage - p. 179 34. Step 4.4. Five Whys for Business Impact - p. 181 35. Step 4.5. Process Impact - p. 182 36. Step 4.6. Risk Analysis - p. 185 37. Step 4.7. Perception of Relevance and Trust - p. 187 38. Business Benefit and Context - p. 187 39. Approach - p. 188 40. Sample Output and Templates - p. 189 41. Step 4.8. Benefit vs. Cost Matrix - p. 191 42. Step 4.9. Ranking and Prioritization - p. 195 43. Step 4.10. Cost of Low-Quality Data - p. 198 44. Step 4.11. Cost-Benefit Analysis and ROI - p. 202 45. Step 4.12. Other Relevant Business Impact Techniques - p. 204 46. Step 5. Identify Root Causes - p. 206 47. Step 5.1. Five Whys for Root Causes - p. 210 48. Step 5.2. Track and Trace - p. 212 49. Step 5.3. Cause-and-Effect/Fishbone Diagram - p. 213 50. Step 5.4. Other Relevant Root Cause Analysis Techniques - p. 218 51. Step 6. Develop Improvement Plans - p. 220 52. Step 7. Prevent Future Data Errors - p. 227 53. Step 8. Correct Current Data Errors - p. 232 54. Step 9. Monitor Controls - p. 236 55. Communicate, Manage, and Engage People Throughout - p. 242 56. Chapter 4 Summary - p. 252 57. Chapter 5. Structuring Your Project - p. 261 58. Chapter 6. Other Techniques and Tools - p. 272 59. Chapter 7. A Few Final Words - p. 305 60. Appendix: Quick References - p. 307 61. Glossary - p. 321 62. List of Figures, Tables, and Templates - p. 331 63. Bibliography - p. 339 64. Index - p. 347 65. About the Author - p. 355
