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Managing Data Integrity for Finance

You're reading from   Managing Data Integrity for Finance Discover practical data quality management strategies for finance analysts and data professionals

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781837630141
Length 434 pages
Edition 1st Edition
Languages
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Author (1):
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Jane Sarah Lat Jane Sarah Lat
Author Profile Icon Jane Sarah Lat
Jane Sarah Lat
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Table of Contents (16) Chapters Close

Preface 1. Part 1: Foundational Concepts for Data Quality and Data Integrity for Finance
2. Chapter 1: Recognizing the Importance of Data Integrity in Finance FREE CHAPTER 3. Chapter 2: Avoiding Common Data Integrity Issues and Challenges in Finance Teams 4. Chapter 3: Measuring the Impact of Data Integrity Issues 5. Part 2: Pragmatic Solutions to Manage Financial Data Quality and Data Integrity
6. Chapter 4: Understanding the Data Integrity Management Capabilities of Business Intelligence Tools 7. Chapter 5: Using Business Intelligence Tools to Fix Data Integrity Issues 8. Chapter 6: Implementing Best Practices When Using Business Intelligence Tools 9. Chapter 7: Detecting Fraudulent Transactions Affecting Financial Report Integrity 10. Part 3: Modern Strategies to Manage the Data Integrity of Finance Systems
11. Chapter 8: Using Database Locking Techniques for Financial Transaction Integrity 12. Chapter 9: Using Managed Ledger Databases for Finance Data Integrity 13. Chapter 10: Using Artificial Intelligence for Finance Data Quality Management 14. Index 15. Other Books You May Enjoy

Data profiling features

Microsoft Power BI has data profiling features that offer simple ways to examine and analyze the data in the Power Query Editor. These are found under the View tab on the ribbon, as shown in Figure 5.22:

Figure 5.22 – Data profiling tools in the View tab

Figure 5.22 – Data profiling tools in the View tab

In the next subsections, we will cover the data profiling features of column quality, column distribution, and column profile.

Column quality

This feature indicates what the column quality of the data is in five categories as shown in Figure 5.23:

Figure 5.23 – Column quality indicators

Figure 5.23 – Column quality indicators

The colors reflect the quality of the data in the columns and make it easier to analyze and examine.

Figure 5.24 shows where the column quality is found in the Power Query Editor:

Figure 5.24 – Column quality

Figure 5.24 – Column quality

We can see from the indicators that the first five columns are valid, while the last three are empty...

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