What are the best data quality controls for financial reporting?

2 Implement data quality checks

The next step is to implement data quality checks that will monitor and verify the quality of your data throughout the data lifecycle. Data quality checks can be performed at different stages, such as data entry, data processing, data integration, data analysis, and data reporting. Data quality checks can be manual or automated, depending on the complexity and volume of your data. For example, you can use data validation rules, data profiling tools, data cleansing software, or data quality dashboards to check your data for errors, inconsistencies, duplicates, outliers, or missing values.

3 Document data quality issues

The third step is to document the data quality issues that you find during your data quality checks, and report them to the relevant stakeholders. Data quality issues should be documented in a structured and standardized way, such as using a data quality issue log or a data quality report. The documentation should include information such as the source, type, impact, and root cause of the data quality issue, as well as the actions taken or planned to resolve it. By documenting the data quality issues, you can track and communicate the status and progress of your data quality improvement efforts, and ensure accountability and transparency.

4 Resolve data quality issues

The fourth step is to resolve the data quality issues that you have documented, and improve the quality of your data. Data quality issues can be resolved in different ways, depending on the nature and severity of the issue. For example, you can correct, delete, or replace the erroneous data, or you can enrich, standardize, or harmonize the inconsistent or incomplete data. You can also implement preventive or corrective actions to avoid or minimize the recurrence of the data quality issue, such as improving your data collection methods, data integration processes, or data governance policies.

5 Review data quality results

The fifth step is to review the results of your data quality improvement actions, and evaluate the impact and effectiveness of your data quality controls. You can review your data quality results by comparing your current data quality metrics with your previous or target metrics, and analyzing the changes and trends in your data quality performance. You can also solicit feedback from your data users, data owners, or data auditors, and assess their satisfaction and confidence in your data quality. By reviewing your data quality results, you can measure and demonstrate the value and benefits of your data quality controls, and identify the areas and opportunities for further improvement.

6 Update data quality controls

The final step is to update your data quality controls based on your data quality results and feedback, and ensure that your data quality controls are relevant and effective for your current and future data needs. You can update your data quality controls by revising your data quality criteria, data quality checks, data quality documentation, or data quality resolution methods, or by introducing new or improved data quality tools, techniques, or best practices. You can also update your data quality controls by aligning them with your changing business objectives, reporting requirements, or data standards. By updating your data quality controls, you can maintain and enhance your data quality management and reporting capabilities, and adapt to the evolving data environment.

7 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?