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Managing Data Integrity for Finance

You're reading from  Managing Data Integrity for Finance

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781837630141
Pages 434 pages
Edition 1st Edition
Languages
Author (1):
Jane Sarah Lat Jane Sarah Lat
Profile icon Jane Sarah Lat

Table of Contents (16) Chapters

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 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

Detecting manual data encoding issues in finance teams

Despite the advancement of automation tools and systems in these past few years, manual encoding errors and mistakes are still prevalent in finance teams and industries globally. Encoding issues and errors affect the reliability and trustworthiness of the data, making the reports generated using this data unfit for use in business decisions. Thus, it is important that these issues are detected early on to prevent this from happening. Here are some of the suggestions and recommendations to help solve these issues.

Utilizing available tools to check for data integrity issues in encoded data

To manage manual encoding data integrity issues, it is important that the collected data is checked and validated using available tools before it is used. This will ensure the accuracy and completeness of the data before it is analyzed or processed. These checks confirm that the data entries use the correct data type or format. At the same...

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