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

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...