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

Recognizing the importance of BI tools

In the past, finance professionals relied heavily on manual processes to collect, analyze, and validate data. For a long time, spreadsheets were the go-to tools for financial modeling. In addition to this, internal audits were conducted through labor-intensive, manual reviews of financial statements and records. As you can see, this approach was not only time-consuming but also prone to human error. Ensuring data integrity and quality was a constant challenge, as even a single oversight could lead to inaccurate financial reporting and compliance issues. If data inconsistencies were identified late in the process, it became difficult to correct them and increased the risk of delaying critical business decisions.

The advent of BI tools has revolutionized the way finance professionals manage data quality and integrity. These tools employ various algorithms and even machine learning techniques to automatically identify data inconsistencies, validate...