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Managing Data Integrity for Finance

You're reading from   Managing Data Integrity for Finance Discover practical data quality management strategies for finance analysts and data professionals

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837630141
Length 434 pages
Edition 1st Edition
Languages
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Author (1):
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Jane Sarah Lat Jane Sarah Lat
Author Profile Icon Jane Sarah Lat
Jane Sarah Lat
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Table of Contents (16) Chapters Close

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 FREE CHAPTER 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

Getting started with SQL

In Chapter 5, Using Business Intelligence Tools to Fix Data Integrity Issues, we worked with spreadsheets to manage and organize data. Spreadsheets can be used to perform various calculations, data processing, and basic data storage operations. However, there are other ways to store and manage information, especially when the data grows in volume. For one, we can make use of databases, which are specialized systems designed to store, retrieve, and manage vast amounts of structured data. They offer a structured framework with clear linkages between data tables, facilitating more effective data management and querying.

Structured Query Language (SQL) is a programming language that’s used to manage relational databases. Two of the most popular types of relational databases are MySQL and PostgreSQL. MySQL, which was developed by Oracle, is the most widely used relational database management system. In this chapter, we will be using PostgreSQL, which is...

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