What this book covers
Chapter 1, Recognizing the Importance of Data Integrity in Finance, gives a quick overview of the concepts relevant to the succeeding chapters in the book.
Chapter 2, Avoiding Common Data Integrity Issues and Challenges in Finance Teams, dives deep into the data integrity issues and challenges faced by different finance teams.
Chapter 3, Measuring the Impact of Data Integrity Issues, teaches you how to develop and generate data quality scorecards using a framework.
Chapter 4, Understanding the Data Integrity Management Capabilities of Business Intelligence Tools, focuses on the common data quality capabilities of business intelligence tools and more popular tools online.
Chapter 5, Using Business Intelligence Tools to Fix Data Integrity Issues, teaches you how to use business intelligence tools in order to solve data integrity issues.
Chapter 6, Implementing Best Practices When Using Business Intelligence Tools, guides you on how to implement various best practices when using business intelligence tools.
Chapter 7, Detecting Fraudulent Transactions Affecting Financial Report Integrity, focuses on processes and strategies to detect fraudulent transactions that affect financial report integrity.
Chapter 8, Using Database Locking Techniques for Financial Transaction Integrity, dives deep into how specific SQL and database techniques prevent transaction data integrity issues.
Chapter 9, Using Managed Ledger Databases for Finance Data Integrity, teaches you how to use managed ledger databases to enforce data integrity in financial systems and applications.
Chapter 10, Using Artificial Intelligence for Finance Data Quality Management, exposes you to artificial intelligence solutions relevant to data quality and data integrity management.
