Book Image

Managing Data Integrity for Finance

By : Jane Sarah Lat
Book Image

Managing Data Integrity for Finance

By: Jane Sarah Lat

Overview of this book

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.
Table of Contents (16 chapters)
1
Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
5
Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
10
Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems

Avoiding Common Data Integrity Issues and Challenges in Finance Teams

In the previous chapter, we learned that financial data integrity management is critical for every organization trying to make data-driven financial decisions and avoid regulatory issues and penalties. That being said, for a finance and data professional, it is important to have the skills to be able to detect and address data integrity issues before they become critical—and even to prevent these issues in the future.

In this chapter, we will build on top of the concepts discussed in the first chapter and cover the following topics to help us avoid common data integrity issues and challenges in finance teams:

  • Detecting manual data encoding issues in financial teams
  • Avoiding common reconciliation errors and mistakes in finance teams
  • Preventing balance sheet data integrity issues
  • Handling data corruption and financial transaction data integrity issues in internal systems and databases
  • ...