Data collection QA process

Quality assurance in data collection means checking if your data is accurate and complete before using it for analysis. Common data issues include duplicate user IDs, impossible values (like negative prices), or missing required fields that could lead to wrong conclusions about user behavior.

Teams use data validation tools like Great Expectations or simple SQL queries in their data warehouses to automatically check their data. For example, these tools can flag if a user's age is logged as 200 years, if purchase amounts are recorded as $0, or if event timestamps are set in the future. Analytics platforms like Amplitude and Mixpanel also provide built-in data quality alerts.

Regular monitoring involves both automated and manual checks. For instance, you might set up alerts for when your daily active user count suddenly drops by 30%, when your event tracking shows duplicate purchases, or when user attributes like “country” contain invalid values. These issues should be documented and fixed quickly to maintain reliable analytics.