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

Measuring the Impact of Data Integrity Issues

Data is considered one of the most important assets for any organization in this digital era. It serves as a foundation for decision-making, business strategy, and reporting, as well as long-term planning. However, given the increasing amount of data being generated, the risk of data integrity issues has also increased significantly. Thus, it is crucial that organizations measure the impact of data integrity issues and ensure that proactive measures are in place to prevent these issues from occurring. Imagine that a company needs information about its inventory purchases to ensure that its production is supported, but finds out that the inventory data has mistakes and errors, making its forecasts inaccurate. This is the reason why there is a need for metrics measured by data quality scorecards.

In this chapter, we will cover the following:

  • Why measure the impact of data integrity issues?
  • Reviewing the relevant data quality...