Summary

In this chapter, we discussed why we need to measure the impact of data integrity issues and learned that these can have direct costs, indirect costs, and even lead to lost opportunities for the business. We then reviewed the relevant data quality metrics for financial data and transactions. We covered the KPIs of completeness, accuracy, consistency, timeliness, and validity, which serve as the criteria for measuring data quality. We then discussed data profiling using a data quality framework, which is a crucial step in determining the quality of data. After that, we prepared sample data quality scorecards using Microsoft Excel and Google Sheets, and discussed functionalities to improve data quality and integrity. Both these tools offer a range of features to address data quality and data integrity.

In the next chapter, we will cover the common data quality management capabilities of business intelligence tools, as well as learn how these tools can be used to manage data...