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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

An introduction to data quality rules

A data quality rule is logic that is applied to each row of a dataset, which can determine whether the row of data is correct or incorrect. Correct data is deemed to have passed the rule, and incorrect data is deemed to have failed the rule – hence, the term failed data, which is used heavily in Chapter 7.

Data quality rules always give a Boolean output – in other words, a row of data always passes or fails.

The following table provides a few (purposefully very simple) examples:

Business logic

Passed row example

Failed row example

The VAT number must be complete for all suppliers.

Any row with any character in this field would pass.

Any row which is “null” or “blank” would fail.

The VAT number...

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