Managing Data Integrity for Finance: Discover practical data quality management strategies for finance analysts

✍ Scribed by Jane Sarah Lat

• Publisher: Packt Publishing Pvt Ltd
• Year: 2024
• Tongue: English
• Leaves: 535
• Edition: 1
• Category: Library

✦ Synopsis

Level up your career by learning best practices for managing the data quality and integrity of your financial data

Key Features
Accelerate data integrity management using artificial intelligence-powered solutions
Learn how business intelligence tools, ledger databases, and database locks solve data integrity issues
Find out how to detect fraudulent transactions affecting financial report integrity

Book Description
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.

What you will learn
Develop a customized financial data quality scorecard
Utilize business intelligence tools to detect, manage, and resolve data integrity issues
Find out how to use managed cloud-based ledger databases for financial data integrity
Apply database locking techniques to prevent transaction integrity issues involving finance data
Discover the methods to detect fraudulent transactions affecting financial report integrity
Use artificial intelligence-powered solutions to resolve various data integrity issues and challenges

Who this book is for
This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it’ll help you grasp the more advanced topics covered in this book.

✦ Table of Contents

Managing Data Integrity for Finance
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Accessing high-resolution images
Conventions used
Get in touch
Share your thoughts
Download a free PDF copy of this book
Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
1
Recognizing the Importance of Data Integrity in Finance
Understanding the impact of data integrity issues in finance
Lack of trust in systems
Damage to reputation
Financial impact
Compliance issues with laws and regulations
A quick tour of concepts relevant to data integrity management
Levenshtein distance
Machine learning
Orphaned records
Financial reporting
Balance sheet
Profit and loss statement
Cash flow statement
Budgeting
Forecasting
Depreciation
Variable cost
Risk management
Insurance
Transaction
Mutual exclusion
Debunking the myths and misconceptions surrounding finance data integrity management
Myth 1 – only large financial organizations are concerned about data integrity
Myth 2 – only finance professionals should be concerned about data integrity
Myth 3 – only internal financial reporting systems are affected by data integrity issues
Myth 4 – processes that improve data integrity are expensive and difficult to implement
Myth 5 – only electronic data is affected by data integrity issues
Summary
Further reading
2
Avoiding Common Data Integrity Issues and Challenges in Finance Teams
Detecting manual data encoding issues in finance teams
Utilizing available tools to check for data integrity issues in encoded data
Regularly audit encoded data
Monitoring and recording changes
Having the right team structure and composition
Putting robust data governance and compliance policies and procedures in place
Avoiding common reconciliation errors and mistakes in finance teams
Understanding common reconciliation errors
Preventing reconciliation errors
Preventing balance sheet data integrity issues
Implementing strong internal controls
Utilizing trustworthy data sources
Well-documented policies and procedures
Employing technology and automation
Handling data corruption and financial transaction data integrity issues in internal systems and databases
Risk assessment of possible data corruption
Establishing detection systems
Implementing preventative measures
Performing regular security audits
Summary
Further reading
3
Measuring the Impact of Data Integrity Issues
Technical requirements
Why measure the impact of data integrity issues?
To manage the risk of basing decisions on bad data
To manage the risk of not complying with regulations
To manage the risk of damage to reputation
Reviewing the relevant data quality metrics for financial data and transactions
Accuracy
Completeness
Consistency
Timeliness
Validity
Data profiling using a data quality framework
Define the criteria for data quality
Gather and evaluate the data
Analyze the quality of your data
Identify and prioritize data quality issues
Create a plan for remediation
Track and gauge the data quality
Preparing a sample data quality scorecard in Microsoft Excel
Establish the data quality metrics to be used
Define the scale for scoring KPIs
Assign a weight for the KPI
Get the overall score for the KPI
Create the template in Excel
Scoring the KPIs
Update the scorecard regularly
Preparing a sample data quality scorecard in Google Sheets
Establish the data quality metrics to be used
Define the scale for scoring the KPIs
Assign a weight for the KPI
Get the overall score for the KPI
Create the template in Google Sheets
Scoring the KPIs
Microsoft Excel and Google Sheets functionalities to improve data quality and integrity
Version control
Collaboration tools
Data validation
Conditional formatting
Summary
Further reading
Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
4
Understanding the Data Integrity Management Capabilities of Business Intelligence Tools
Technical requirements
Recognizing the importance of BI tools
Exploring common data quality management capabilities of BI tools
Data profiling
Data cleansing
Data validation
Data lineage
Data governance
Reviewing the most popular BI tools and how to get started with them
Microsoft Power BI
Tableau by Salesforce
Alteryx analytics cloud platform
Summary
Further reading
5
Using Business Intelligence Tools to Fix Data Integrity Issues
Technical requirements
Managing data integrity issues with BI tools
Ensuring consistent data type formatting
Data profiling features
Column quality
Column distribution
Column profile
Data cleansing methods
Removing empty cells
Removing duplicates
Identifying data outliers
Managing relationships in data models
Dealing with large financial datasets using data validation
Summary
Further reading
6
Implementing Best Practices When Using Business Intelligence Tools
Technical requirements
Handling confusing date convention formats
Using data visualization to identify data outliers
Visualizing using a scatter chart
Visualizing using a histogram
Managing orphaned records
Identifying orphaned records in Power BI
Identifying orphaned records in Alteryx
Summary
Further reading
7
Detecting Fraudulent Transactions Affecting Financial Report Integrity
Technical requirements
Understanding the major causes of fraud
Common myths and misconceptions about financial fraud
Myth 1—the impact of fraud is insignificant
Myth 2—fraud is very hard to detect
Myth 3—prosecution completely deters fraud
Myth 4—preventing fraud is only important for big institutions
Myth 5—large companies are the common targets of fraud
Interpreting financial reports
Horizontal or trend analysis
Vertical analysis
Competitor and industry analysis
Cash flow analysis
Learning how fraudulent transactions affect overall financial report integrity
Fictitious revenues
Improper capitalization of expenses
Misrepresentation of liabilities and debt
Detecting and preventing fraudulent transactions and anomalies
Tone at the top
Implementing strong internal controls
Management review
Ratio analysis
Utilizing data analytics and machine learning in fraud detection
Summary
Further reading
Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems
8
Using Database Locking Techniques for Financial Transaction Integrity
Technical requirements
Getting started with SQL
Installing PostgreSQL
Creating a database
Creating a table
Inserting data into the table
Learning how race conditions impact the transaction integrity of financial systems
Reviewing how database locks prevent financial transaction integrity issues
Guaranteeing transaction integrity with database locks
Best practices when using database locks
Summary
Further reading
9
Using Managed Ledger Databases for Finance Data Integrity
Technical requirements
Introduction to ledger databases
Creating an AWS account
Creating an S3 bucket
Creating the Amazon QLDB ledger
Reviewing the internals of ledger databases
Getting the digest
Creating a table
Using the PartiQL editor
Generating a document
Saving and retrieving a query
Viewing the data in the table
Loading saved queries
Nesting automatically
Understanding how ledger databases prevent data integrity issues
Verifying the document
Updating the transaction
Obtaining the digest
Verifying the results
Deleting records from the ledger
Working with history and data
Exporting the journal
Cleaning up
Exploring the best practices when using ledger databases
Summary
Further reading
10
Using Artificial Intelligence for Finance Data Quality Management
Technical requirements
Introduction to AI
Applications of AI in finance
Detecting anomalies in financial transaction data
Handling missing financial reporting data with AI
Best practices when using AI for data integrity management
Summary
Further reading
Index
Why subscribe?
Other Books You May Enjoy
Packt is searching for authors like you
Share your thoughts
Download a free PDF copy of this book