What are the most common data validation techniques used in financial reporting?

2 Data rules and logic

Another data validation technique is to apply data rules and logic to test the validity and consistency of data across different sources and fields. For example, you can check that the sum of debits and credits equals zero, that the balances of accounts match the statements, and that the ratios and percentages are calculated correctly. You can use conditional statements or functions in your spreadsheet or database software to perform these checks, or you can use a data validation tool that allows you to define and apply custom rules and logic.

3 Data reconciliation and comparison

A third data validation technique is to reconcile and compare data from different sources and periods to identify any discrepancies or anomalies. For example, you can compare the data from your internal systems with the data from external sources, such as bank statements, invoices, or tax returns. You can also compare the data from different periods, such as month-to-month, quarter-to-quarter, or year-to-year. You can use pivot tables, charts, or reports in your spreadsheet or database software to perform these comparisons, or you can use a data validation tool that provides visual and interactive dashboards and alerts.

4 Data review and approval

A fourth data validation technique is to review and approve data before finalizing and publishing the financial reports. This involves having a second pair of eyes or a third-party auditor to examine the data and verify its accuracy, completeness, and consistency. You can use comments, annotations, or feedback features in your spreadsheet or database software to facilitate this review process, or you can use a data validation tool that integrates with your workflow and audit trail.

5 Data quality and governance

A fifth data validation technique is to establish and maintain data quality and governance standards and policies for your financial reporting. This involves defining the roles and responsibilities of data owners, users, and stewards, as well as the procedures and protocols for data collection, storage, access, security, and retention. You can use documentation, training, or certification programs in your organization to communicate and enforce these standards and policies, or you can use a data validation tool that supports data quality and governance best practices.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?