Data Validation in Google Analytics

Google today published a video on data validation in Google Analytics. Proper data validation is crucial for ensuring the accuracy and reliability of your Google Analytics data.

By identifying and addressing any data discrepancies, you can gain valuable insights into your website traffic and make informed decisions about your marketing strategies.

Krista Seiden, a Google Analytics expert, is the host of the video. In this video, she covers the following topics:

• How to validate your data
• What does "not set" or "empty name" mean?
• What does the "other row" mean?
• How to troubleshoot data discrepancies and trend oscillations

Key Techniques for Data Validation

1. Utilize Google Tag Manager's Preview Mode: Employ Google Tag Manager's built-in preview mode to test your tags and ensure that data is firing correctly. This will help you identify any potential issues before they impact your analytics reports.
2. Monitor Realtime Data: Regularly check the Realtime report to observe real-time traffic patterns and ensure that events are being collected as expected. This provides immediate feedback on the health of your data collection setup.
3. Identify "Not Set" Values: Look for "not set" values in your reports, as these often indicate setup issues that prevent Google Analytics from receiving and reporting data for specific dimensions. Address these issues promptly to maintain data integrity.
4. Detect "Other" Rows: Be wary of "other" rows appearing in your reports, as these typically arise when the number of rows exceeds the table's row limit. Consider adjusting your data collection settings or using alternative reporting methods to handle large datasets.

Addressing Undefined Values

"Not set" values often stem from setup issues that prevent Google Analytics from receiving and reporting data for specific dimensions. To resolve these issues, review your data collection settings and ensure that all necessary tags are properly configured.

"Other" rows, on the other hand, typically appear when the number of rows in a table exceeds the table's row limit. To address this, you can consider adjusting your data collection settings to capture fewer data points or using alternative reporting methods that are better suited for handling large datasets.

Identifying Data Discrepancies and Trend Oscillations

1. Leverage Report Snapshots: Employ the Report Snapshot feature to identify sudden spikes or dips in your data. This can help you pinpoint potential anomalies or data quality issues.
2. Utilize Explore Tool: Delve into the Explore tool to drill down into the source of spikes or dips in your data. This provides a more granular understanding of the underlying factors contributing to these fluctuations.
3. Examine Dimensions: Employ dimensions like country, browser type, browser version, device type, and operating system to gain insights into the legitimacy of your traffic. This can help you identify and address any suspicious or fraudulent activity.
4. Create Segments: Create segments to exclude traffic that you don't expect, such as bots or automated traffic sources. This can help you focus on the more relevant and reliable data points.
5. Recognize Expected Trends: Be aware of expected trends in your data, such as dips in traffic over the weekend or during holidays. This helps you distinguish between anomalies and normal fluctuations.