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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

Data profiling using a data quality framework

A crucial step in determining the quality of your data is data profiling. This entails examining your data to comprehend its composition and linkages. We will be discussing the data profiling features of business intelligence tools in the next two chapters. In this section, we will be using a data quality framework to accomplish data profiling by performing the general steps seen in Figure 3.2:

Figure 3.2 – General steps for data profiling

Figure 3.2 – General steps for data profiling

Let’s go through this, step by step.

Define the criteria for data quality

Determine the relevant data quality metrics that are important to the business. These are the indicators of accuracy, completeness, consistency, timeliness, and validity that we covered earlier in the chapter. To which metrics we will give more importance will be context-specific and depend on what the company aims to achieve.

Continuing our scenario at the start of this chapter...

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