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Managing Data Integrity for Finance

Managing Data Integrity for Finance

By : Jane Sarah Lat
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Managing Data Integrity for Finance

Managing Data Integrity for Finance

5 (4)
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)
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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

Dealing with large financial datasets using data validation

When dealing with large financial datasets, the tendency is to allow a certain percentage of incorrectness or inaccuracy due to the effort needed to clean the data. However, outliers in the data will affect analysis and report generation, especially if these outliers and errors aren’t cleaned due to time-saving methods in the overall process. That said, guidelines should be created on what the thresholds are in advance for each column and set of records. These guidelines then need to be converted into automated processes available in the BI tool.

An example would be a guideline where column values cannot be negative, cannot exceed a certain threshold, or should be a particular set of values. This guideline would then be converted into a rule that can then be used to automatically detect data issues. Once the incorrect records have been tagged accordingly, these records can be analyzed and corrected manually. In some...

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