Managing Data Integrity for Finance : Discover Practical Data Quality Management Strategies for Finance Analysts and Data Professionals / Jane Sarah Lat.

Format:

Book

Author/Creator:

Lat, Jane Sarah, author.

Language:

English

Subjects (All):

Finance--Data processing.

Finance.

Finance--Computer programs--Security measures.

Computer security.

Physical Description:

1 online resource (434 pages)

Edition:

First edition.

Place of Publication:

Birmingham, UK : Packt Publishing, [2024]

Biography/History:

Lat Jane Sarah: Jane Sarah Lat is a finance consultant with over 14 years of experience in financial management and analysis for multiple blue-chip multinational organizations. In addition to being a Certified Management Accountant (CMA U. S. ) and having a Graduate Diploma in Chartered Accounting (GradDipCA), she also holds various technical certifications, including Microsoft Certified Data Analyst Associate and Advanced Proficiency in KNIME Analytics Platform. Over the past few years, she has been sharing her experience and expertise at international conferences to discuss practical strategies on finance, data analysis, and management accounting. She is also president of the Institute of Management Accountants (IMA) Australia and New Zealand chapter.

Summary:

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.

Contents:

Cover

Title Page

Copyright

Contributors

Table of Contents

Preface

Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance

Chapter 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

Chapter 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

Chapter 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

Define the scale for scoring the KPIs

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

Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity

Chapter 4: Understanding the Data Integrity Management Capabilities of Business Intelligence Tools

Recognizing the importance of BI tools

Exploring common data quality management capabilities of BI tools

Data profiling

Data cleansing

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

Chapter 5: Using Business Intelligence Tools to Fix Data Integrity Issues

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

Chapter 6: Implementing Best Practices When Using Business Intelligence Tools

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

Chapter 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

Management review

Ratio analysis

Utilizing data analytics and machine learning in fraud detection

Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems

Chapter 8: Using Database Locking Techniques for Financial Transaction Integrity

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

Chapter 9: Using Managed Ledger Databases for Finance Data Integrity

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

Chapter 10: Using Artificial Intelligence for Finance Data Quality Management

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

Index

About PACKT

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Notes:

Description based on print version record.

ISBN:

9781837636099

1837636095

OCLC:

1419963326