Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Managing Data Integrity for Finance

You're reading from  Managing Data Integrity for Finance

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781837630141
Pages 434 pages
Edition 1st Edition
Languages
Author (1):
Jane Sarah Lat Jane Sarah Lat
Profile icon Jane Sarah Lat

Table of Contents (16) Chapters

Preface 1. Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
2. Chapter 1: Recognizing the Importance of Data Integrity in Finance 3. Chapter 2: Avoiding Common Data Integrity Issues and Challenges in Finance Teams 4. Chapter 3: Measuring the Impact of Data Integrity Issues 5. Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
6. Chapter 4: Understanding the Data Integrity Management Capabilities of Business Intelligence Tools 7. Chapter 5: Using Business Intelligence Tools to Fix Data Integrity Issues 8. Chapter 6: Implementing Best Practices When Using Business Intelligence Tools 9. Chapter 7: Detecting Fraudulent Transactions Affecting Financial Report Integrity 10. Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems
11. Chapter 8: Using Database Locking Techniques for Financial Transaction Integrity 12. Chapter 9: Using Managed Ledger Databases for Finance Data Integrity 13. Chapter 10: Using Artificial Intelligence for Finance Data Quality Management 14. Index 15. Other Books You May Enjoy

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £13.99/month. Cancel anytime}