What are effective ways to communicate data quality issues to stakeholders?

1 Identify the root cause

Before you communicate data quality issues to your stakeholders, you need to identify the root cause of the problem. Is it a technical issue, such as a broken data pipeline, a missing value, or a wrong format? Is it a human error, such as a typo, a mislabeling, or a misunderstanding? Is it a business issue, such as a change in the data source, a new requirement, or a mismatched expectation? By identifying the root cause, you can provide more context, explain the impact, and suggest the best solution.

2 Choose the right channel

Depending on the urgency, severity, and audience of the data quality issue, you need to choose the right channel to communicate it. For example, if the issue is critical and affects a large number of stakeholders, you may want to use a formal and visible channel, such as an email, a dashboard, or a presentation. If the issue is minor and affects a small group of stakeholders, you may want to use an informal and direct channel, such as a chat, a phone call, or a meeting. The goal is to reach your stakeholders effectively and efficiently, without causing unnecessary alarm or confusion.

3 Use clear and concise language

When you communicate data quality issues to your stakeholders, you need to use clear and concise language that avoids jargon, ambiguity, and technical details. You should focus on the what, why, and how of the issue, rather than the how much or how often. For example, instead of saying "The data quality score for the sales report is 75%, which is below the threshold of 80%", you could say "The sales report has some missing and inaccurate data, because of a system outage that affected the data collection process. We are working on fixing the issue and updating the report as soon as possible." This way, you can convey the message more clearly and convincingly.

4 Provide visual and contextual evidence

Another effective way to communicate data quality issues to your stakeholders is to provide visual and contextual evidence that supports your message. You can use charts, graphs, tables, screenshots, or other visual aids that show the data quality issue, its impact, and its resolution. You can also provide contextual evidence that relates the data quality issue to the business goal, the decision-making process, or the stakeholder's interest. For example, you could say "The data quality issue affects the accuracy of the revenue forecast, which is important for planning the budget and allocating resources. Here is a chart that shows the difference between the actual and the expected revenue, and how we can improve it by fixing the data quality issue."

5 Follow up and follow through

Finally, you need to follow up and follow through on your communication of data quality issues to your stakeholders. You need to keep them updated on the status, progress, and outcome of the issue resolution. You need to acknowledge their feedback, questions, and concerns, and address them promptly and professionally. You need to verify and validate the data quality after the issue is resolved, and share the results and lessons learned with your stakeholders. By following up and following through, you can build trust, credibility, and collaboration with your stakeholders, and improve your data quality and decision-making in the long run.