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

Best practices when using AI for data integrity management

Here are some of the best practices you can make use of when utilizing AI to improve the quality and integrity of your data:

  • Clearly state your goals: Before you make use of an AI model, ensure that you are clear about what you want to accomplish. Knowing this will help guide you in your decisions in selecting the AI tool or developing the model to use for data cleansing, validation, governance, and compliance.
  • Create clear and effective prompts: When using AI models, especially for generating content or answers to questions, it’s essential to prepare prompts that are concise and specific to the desired outcome. This gives rise to the importance of prompt engineering, which is a method of developing well-defined prompts to help ensure that the AI or language model understands the context and your requirements to produce relevant and accurate results.

Note

Creating well-structured prompts is key...