How do you manage stakeholder queries about data quality in performance reporting?

2 Validate and verify data sources

Once you have defined your data quality criteria, you need to validate and verify your data sources. Validation means checking that the data sources are appropriate and relevant for your project, and that they meet the data quality criteria. Verification means checking that the data sources are accurate and reliable, and that they have not been manipulated or corrupted. You can use various methods to validate and verify your data sources, such as cross-referencing, auditing, sampling, testing, and peer-reviewing. You should also document your validation and verification process and results, and share them with your stakeholders, so that they can trust your data sources.

3 Address data quality issues

Despite your best efforts, you may encounter some data quality issues during your performance reporting process. These issues may be due to human errors, technical glitches, missing values, outliers, or inconsistencies. You should not ignore or hide these issues, as they may affect the credibility and validity of your performance report. Instead, you should address them as soon as possible, by correcting, cleaning, or adjusting the data, or by explaining the reasons and implications of the issues. You should also document your data quality issues and actions, and communicate them to your stakeholders, so that they can understand the impact and limitations of your data.

4 Present data clearly and transparently

The final step in managing stakeholder queries about data quality in performance reporting is to present your data clearly and transparently. You should use appropriate and consistent formats, charts, tables, and graphs to display your data, and avoid cluttering or confusing your audience with too much or too little information. You should also use clear and concise language to explain your data, and highlight the key findings, insights, and recommendations. Most importantly, you should be transparent about your data quality criteria, sources, issues, and actions, and provide references and evidence to support your data. You should also invite feedback and questions from your stakeholders, and be prepared to answer them with confidence and honesty.

5 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?