How do you tell data sources about data quality issues?

1 Assess the impact

Before you report any data quality issues, you need to assess how they affect your data analysis and the business objectives. You need to determine the severity, frequency, and scope of the issues, and how they impact the data validity, completeness, consistency, timeliness, and relevance. You also need to estimate the effort and resources needed to resolve the issues, and the potential benefits and risks of doing so.

2 Identify the root cause

Next, you need to identify the root cause of the data quality issues, and not just the symptoms. You need to understand why the issues occurred, and whether they are due to technical, human, or organizational factors. You can use tools such as data profiling, data lineage, data quality rules, or data quality dashboards to help you diagnose the issues. You can also consult with other data users, experts, or stakeholders to get more insights.

3 Document the issues

Once you have identified the impact and the root cause of the data quality issues, you need to document them in a clear, concise, and structured way. You need to provide evidence and examples of the issues, such as screenshots, reports, or code snippets. You also need to describe the expected and actual results, the steps to reproduce the issues, and the criteria to validate the solutions. You can use a template or a tool to help you create a data quality report or a data quality issue log.

4 Choose the right channel

The next step is to choose the right channel to communicate the data quality issues to the data sources. You need to consider the urgency, complexity, and sensitivity of the issues, and the preferences and availability of the data sources. You can use different channels, such as email, phone, chat, or video call, depending on the situation. You also need to prepare the appropriate format and level of detail for your communication, such as a summary, a presentation, or a demo.

5 Provide feedback

When you communicate the data quality issues to the data sources, you need to provide feedback in a respectful, constructive, and collaborative way. You need to acknowledge the value and effort of the data sources, and avoid blaming or criticizing them. You also need to explain the impact and the root cause of the issues, and suggest possible solutions or actions. You need to invite the data sources to share their perspectives, concerns, or questions, and listen to them attentively.

6 Follow up

The last step is to follow up with the data sources until the data quality issues are resolved. You need to monitor the progress and status of the issues, and provide updates and reminders as needed. You also need to test and validate the solutions or actions implemented by the data sources, and confirm that they meet the expectations and requirements. You need to thank the data sources for their cooperation and support, and share the results and benefits of the data quality improvement.

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?