Managing Data Integrity for Finance: Discover practical data quality management strategies for finan

Level up your career by learning best practices for managing the data quality and integrity of your financial data

• Accelerate data integrity management using artificial intelligence-powered solutions
• Learn how business intelligence tools, ledger databases, and database locks solve data integrity issues
• Find out how to detect fraudulent transactions affecting financial report integrity

Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.

The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.

After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.

• Develop a customized financial data quality scorecard
• Utilize business intelligence tools to detect, manage, and resolve data integrity issues
• Find out how to use managed cloud-based ledger databases for financial data integrity
• Apply database locking techniques to prevent transaction integrity issues involving finance data
• Discover the methods to detect fraudulent transactions affecting financial report integrity
• Use artificial intelligence-powered solutions to resolve various data integrity issues and challenges

This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it'll help you grasp the more advanced topics covered in this book.

1. Recognizing the Importance of Data Integrity in Finance
2. Avoiding Common Data Integrity Issues and Challenges in Finance Teams
3. Measuring the Impact of Data Integrity Issues
4. Understanding the Data Integrity Management Capabilities of Business Intelligence Tools
5. Using Business Intelligence Tools to Fix Data Integrity Issues
6. Implementing Best Practices When Using Business Intelligence Tools
7. Detecting Fraudulent Transactions Affecting Financial Report Integrity
8. Using Database Locking Techniques for Financial Transaction Integrity
9. Using Managed Ledger Databases for Finance Data Integrity
10. Using Artificial Intelligence for Finance Data Quality Management