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

Handling confusing date convention formats

One of the most common data integrity issues encountered when dealing with date time values involves the inconsistent positioning of the month and date values in data entries and transactions. In some countries, mm/dd/yyyy is used for the date format. In other countries, dd/mm/yyyy is used. Of course, the number of days (that is, 01 to 31) exceeds the number of months (that is, 01 to 12). However, what if the record stored in the sheet or database is 03/06/1990? If the assumed format is mm/dd/yyyy, then 03/06/1990 will be interpreted as March 6, 1990. On the other hand, if the assumed format is dd/mm/yyyy, then the same date will be interpreted as June 3, 1990 instead.

Now, we have a data integrity issue when a single column involves both formats. There are a variety of reasons this could happen and one of the possible causes is if records from multiple data sources were merged into a single sheet or table without taking into account the...