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Managing Data Integrity for Finance

You're reading from   Managing Data Integrity for Finance Discover practical data quality management strategies for finance analysts and data professionals

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837630141
Length 434 pages
Edition 1st Edition
Languages
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Author (1):
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Jane Sarah Lat Jane Sarah Lat
Author Profile Icon Jane Sarah Lat
Jane Sarah Lat
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
2. Chapter 1: Recognizing the Importance of Data Integrity in Finance FREE CHAPTER 3. Chapter 2: Avoiding Common Data Integrity Issues and Challenges in Finance Teams 4. Chapter 3: Measuring the Impact of Data Integrity Issues 5. Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
6. Chapter 4: Understanding the Data Integrity Management Capabilities of Business Intelligence Tools 7. Chapter 5: Using Business Intelligence Tools to Fix Data Integrity Issues 8. Chapter 6: Implementing Best Practices When Using Business Intelligence Tools 9. Chapter 7: Detecting Fraudulent Transactions Affecting Financial Report Integrity 10. Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems
11. Chapter 8: Using Database Locking Techniques for Financial Transaction Integrity 12. Chapter 9: Using Managed Ledger Databases for Finance Data Integrity 13. Chapter 10: Using Artificial Intelligence for Finance Data Quality Management 14. Index 15. Other Books You May Enjoy

Summary

In this chapter, we discussed how to manage data integrity issues with BI tools. At the beginning, we learned how to ensure we have consistent data type formats in our working files. Then, we covered data profiling features such as column quality, column distribution, and column profiling. After that, we worked out how to cleanse the data. In addition to this, we learned how to identify data outliers as well as how to manage relationships in data models. Lastly, we went through how to deal with large financial datasets using data validation. We have explored really powerful techniques and concepts in this chapter. You should be feeling confident about what you have learned, knowing that you can use these techniques whenever you need to clean the data prior to analysis.

The topics in the next chapter will be pretty exciting! We will continue our journey with these BI tools and cover how to implement best practices!

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