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

Data cleansing methods

As we mentioned in the previous chapter, in the Exploring common data quality management capabilities of BI tools section, data cleansing is a key step in improving the integrity of the data. In this section, we will continue our hands-on examples to cleanse the dataset using Power BI Desktop.

Removing empty cells

If we examine the column statistics in Figure 5.34, our total transaction count is 1,464, no errors have been found, and there are 335 empty cells. If we scroll to the bottom of the table, we can find that rows 1,130 up to 1,464 have null values or empty cells. We can remove these by going to the Home tab, clicking on Remove Rows, and selecting Remove Blank Rows, as shown in Figure 5.35:

Figure 5.35 – Removing empty cells

Figure 5.35 – Removing empty cells

If we scroll down the data table after this step, we can see that the table now ends on row 1,129 and the empty cells have been removed, as illustrated in Figure 5.36:

Figure 5.36 – After removing empty cells
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