What do you do if your data quality in Analytics is compromised?

1 Assess the impact

The first step is to assess the impact of the data quality issue on your analytics goals and metrics. How severe is the problem? How long has it been going on? How many data sources, dimensions, segments, or reports are affected? How does it affect your key performance indicators (KPIs), conversions, or revenue? You can use tools like Google Analytics Audit or Data Studio to check for anomalies, discrepancies, or inconsistencies in your data.

2 Identify the root cause

The next step is to identify the root cause of the data quality issue. What is the source of the error or discrepancy? Is it related to your data collection, processing, or reporting methods? Is it due to a change in your website, app, or marketing campaign settings? Is it caused by a technical issue, such as a broken link, a tracking code error, or a server outage? Is it a result of a human error, such as a typo, a misconfiguration, or a wrong filter? Is it a deliberate attempt to manipulate or sabotage your data, such as spam, bots, or hackers? You can use tools like Google Tag Assistant, Google Analytics Debugger, or Google Search Console to troubleshoot and verify your data quality.

3 Implement the solution

The third step is to implement the solution to fix the data quality issue. Depending on the nature and scope of the problem, you may need to take different actions, such as correcting, updating, or deleting the faulty data, restoring the missing or corrupted data, adjusting or adding the tracking codes or tags, applying or removing the filters or segments, or securing or blocking the unwanted traffic or data. You can use tools like Google Analytics Data Import, Google Analytics Data Repair, or Google Analytics Filters to implement the solution.

4 Monitor the results

The fourth step is to monitor the results of your solution and ensure that the data quality issue is resolved. How effective was your solution? How quickly did it take effect? How did it improve your data quality and accuracy? How did it impact your analytics goals and metrics? You can use tools like Google Analytics Real-Time, Google Analytics Intelligence, or Google Analytics Alerts to monitor and measure the results.

5 Document the process

The fifth step is to document the process and learn from the experience. What did you learn from the data quality issue and how did you solve it? What were the challenges and opportunities you faced? What were the best practices and recommendations you followed? How can you prevent or minimize the occurrence of similar issues in the future? You can use tools like Google Analytics Annotations, Google Analytics Notes, or Google Analytics Dashboard to document and share the process.

6 Improve the data quality

The final step is to improve the data quality and maintain it at a high level. How can you improve the data quality and reliability of your analytics? What are the data quality standards and policies you need to follow? What are the data quality tools and techniques you need to use? How can you automate or streamline the data quality checks and audits? How can you educate and train your team on data quality best practices and principles? You can use tools like Google Analytics Data Quality Checklist, Google Analytics Data Quality Scorecard, or Google Analytics Academy to improve and maintain the data quality.

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?