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  • Book Overview & Buying Managing Data Integrity for Finance
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Managing Data Integrity for Finance

Managing Data Integrity for Finance

By : Jane Sarah Lat
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Managing Data Integrity for Finance

Managing Data Integrity for Finance

5 (4)
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)
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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

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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