Managing Data Integrity for Finance: Practical data quality management strategies for finance analysts and data professionals

Managing Data Integrity for Finance: Practical data quality management strategies for finance analysts and data professionals
by: Jane Sarah Lat (Author)
Publisher:Packt Publishing – ebooks Account
Publication Date: 9 Feb. 2024
Language:English
Print Length:374 pages
ISBN-10:1837630143
ISBN-13:9781837630141
Book Description
Level up your career by learning the best practices for managing data quality and integrity of your financial dataKey FeaturesUnderstand the financial data quality metrics available to measure the quality of finance dataPerform data quality management operations using a variety of data tools for financeLearn how ledger databases, database locks, and machine learning solve data integrity challengesBook DescriptionThere is a big gap between the finance and technology sectors when it comes to the proper analysis and management of finance data and the proper implementation of data systems. More companies around the world have been using finance and operational data to make business decisions. That said, data quality and data integrity play a crucial role in the success and effectiveness of those organizations.The first part covers the foundational concepts for data quality and data integrity relevant to finance domains such as banking, insurance, portfolio management, credit and risk management, and corporate finance. The second part focuses on having the reader use several data tools and platforms to manage data quality and data integrity issues on sample finance data. Finally, the book covers intermediate and advanced solutions including blockchain solutions, database ledgers, and database locks to manage data integrity for transactional finance systems.After finishing this hands-on book, you will be able to recommend various finance and technical solutions to solve transaction and data integrity challenges experienced by finance organizations.What you will learnDevelop data quality metrics to measure data integrityPerform finance data quality management operations using data toolsDiscover how ledger databases help prevent data integrity issuesApply database techniques to prevent transaction data integrity issuesUse machine learning methods to manage data integrityImplement data quality initiatives for financial projectsHandle missing financial reporting data using Generative AIImplement best practices using Generative AI in Data Quality ManagementWho this book is forThis book is intended for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks, tools, and strategies. The reader is expected to have a basic understanding of finance concepts, accounting, and data analysis. Basic knowledge of finance management is not required but will help grasp the intermediate topics of the book.Table of ContentsRecognizing the Importance of Data Integrity in FinanceAvoiding Common Data Integrity Issues and Challenges in Finance ServicesMeasuring the Financial Impact of Data Integrity IssuesUnderstanding the Data Integrity Management Capabilities of Business Intelligence ToolsUsing Business Intelligence Tools to Fix Data Integrity IssuesImplementing the Best Practices when Using Business Intelligence ToolsDetecting Fraudulent Transactions Affecting Financial Report IntegrityUsing Database Locking Techniques for Financial Transaction IntegrityUsing Managed Ledger Databases for Finance Data IntegrityUsing Generative AI for Finance Data Quality Management
About the Author
Level up your career by learning the best practices for managing data quality and integrity of your financial dataKey FeaturesUnderstand the financial data quality metrics available to measure the quality of finance dataPerform data quality management operations using a variety of data tools for financeLearn how ledger databases, database locks, and machine learning solve data integrity challengesBook DescriptionThere is a big gap between the finance and technology sectors when it comes to the proper analysis and management of finance data and the proper implementation of data systems. More companies around the world have been using finance and operational data to make business decisions. That said, data quality and data integrity play a crucial role in the success and effectiveness of those organizations.The first part covers the foundational concepts for data quality and data integrity relevant to finance domains such as banking, insurance, portfolio management, credit and risk management, and corporate finance. The second part focuses on having the reader use several data tools and platforms to manage data quality and data integrity issues on sample finance data. Finally, the book covers intermediate and advanced solutions including blockchain solutions, database ledgers, and database locks to manage data integrity for transactional finance systems.After finishing this hands-on book, you will be able to recommend various finance and technical solutions to solve transaction and data integrity challenges experienced by finance organizations.What you will learnDevelop data quality metrics to measure data integrityPerform finance data quality management operations using data toolsDiscover how ledger databases help prevent data integrity issuesApply database techniques to prevent transaction data integrity issuesUse machine learning methods to manage data integrityImplement data quality initiatives for financial projectsHandle missing financial reporting data using Generative AIImplement best practices using Generative AI in Data Quality ManagementWho this book is forThis book is intended for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks, tools, and strategies. The reader is expected to have a basic understanding of finance concepts, accounting, and data analysis. Basic knowledge of finance management is not required but will help grasp the intermediate topics of the book.Table of ContentsRecognizing the Importance of Data Integrity in FinanceAvoiding Common Data Integrity Issues and Challenges in Finance ServicesMeasuring the Financial Impact of Data Integrity IssuesUnderstanding the Data Integrity Management Capabilities of Business Intelligence ToolsUsing Business Intelligence Tools to Fix Data Integrity IssuesImplementing the Best Practices when Using Business Intelligence ToolsDetecting Fraudulent Transactions Affecting Financial Report IntegrityUsing Database Locking Techniques for Financial Transaction IntegrityUsing Managed Ledger Databases for Finance Data IntegrityUsing Generative AI for Finance Data Quality Management