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

Exploring common data quality management capabilities of BI tools

In the previous section, we discussed how important BI tools are to finance professionals and organizations. Now, in this section, we will focus on various data quality management capabilities available in most BI tools. Understanding these features in depth will help us choose the right tool when dealing with finance data integrity issues.

Data profiling

Data profiling involves examining, analyzing, and producing insightful summaries of data. We covered the general steps for data profiling in Chapter 3, Measuring the Impact of Data Integrity Issues, in the Data profiling using a data quality framework section.

This typically involves the following steps:

  1. Defining the criteria for data quality
  2. Gathering and evaluating the data
  3. Analyzing data quality
  4. Identifying and prioritizing data quality issues
  5. Creating a remediation plan
  6. Tracking and gauging data quality

Note

We will...