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Practical Data Quality

You're reading from   Practical Data Quality Learn practical, real-world strategies to transform the quality of data in your organization

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781804610787
Length 318 pages
Edition 1st Edition
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Author (1):
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Robert Hawker Robert Hawker
Author Profile Icon Robert Hawker
Robert Hawker
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Table of Contents (16) Chapters Close

Preface 1. Part 1 – Getting Started
2. Chapter 1: The Impact of Data Quality on Organizations FREE CHAPTER 3. Chapter 2: The Principles of Data Quality 4. Chapter 3: The Business Case for Data Quality 5. Chapter 4: Getting Started with a Data Quality Initiative 6. Part 2 – Understanding and Monitoring the Data That Matters
7. Chapter 5: Data Discovery 8. Chapter 6: Data Quality Rules 9. Chapter 7: Monitoring Data Against Rules 10. Part 3 – Improving Data Quality for the Long Term
11. Chapter 8: Data Quality Remediation 12. Chapter 9: Embedding Data Quality in Organizations 13. Chapter 10: Best Practices and Common Mistakes 14. Index 15. Other Books You May Enjoy

The data quality improvement cycle

Different organizations clearly have very different challenges. They are in different locations around the world, with different local laws and regulations. They are in different industries – some closely regulated and some with minimal regulation. However, in my experience, the process that needs to be followed to improve data quality is consistent across all organizations. I have seen success with this process in multiple industries and various parts of the world.

The process I will outline is not “rocket science” – it is a logical and simple approach that I believe most organizations will benefit from. It is the basis of the rest of the book’s structure. Each chapter from now on (with the exception of Chapters 4 and 10) will focus on a particular part of the data quality improvement cycle.

The following diagram provides an overview of the cycle:

Figure 2.5 – The data quality improvement cycle

Figure 2.5 – The data quality...

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