Book Image

Managing Data Integrity for Finance

By : Jane Sarah Lat
Book Image

Managing Data Integrity for Finance

By: Jane Sarah Lat

Overview of this book

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.
Table of Contents (16 chapters)
1
Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
5
Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
10
Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems

Using Business Intelligence Tools to Fix Data Integrity Issues

In the previous chapter, we discussed the data integrity features available in the most common business intelligence (BI) tools. We also covered how to get started with using them. In this chapter, we will delve into how to manage data integrity issues when using Microsoft Power BI, Tableau from Salesforce, and Alteryx. We will build on top of what we’ve learned already and explore various features to ensure the integrity of financial data.

That said, these are the topics that we will explore in this chapter:

  • Managing data integrity issues with BI tools
  • Data profiling features
  • Data cleansing methods
  • Managing relationships in data models
  • Dealing with large financial datasets using data validation

By the end of this chapter, you will have gained a deeper understanding of what these BI tools are capable of, particularly in terms of fixing data integrity issues.