Data Quality & Governance

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.

Data privacy laws differ across countries and regions, affecting how you collect and handle user data. GDPR in Europe requires explicit user consent and gives users the right to access or delete their data.^[2] CCPA in California focuses on giving users control over data sales^[3], while LGPD in Brazil combines aspects of both.^[4]

Each regulation has specific requirements for data handling. For instance, GDPR mandates that you document the purpose of data collection, limit data retention periods, and report breaches within 72 hours. Different regions also have varying rules about data localization — some require user data to be stored on servers within their borders.

Compliance for such laws isn't optional — fines can be substantial. In fact, GDPR violations can cost up to €20 million or 4% of the company's global revenue.^[5] Beyond fines, compliance builds user trust and protects your business. For compliance, start by mapping what data you collect and where it’s stored. Next, document all processing activities, and implement necessary security measures like data encryption and access controls.

Data retention is about deciding how long to keep different types of user data before deleting it. Think of it like cleaning your closet — you keep important documents but throw away old receipts you don't need anymore. In products, some data needs to be kept longer than others.

Common retention rules might be: keeping account information while users are active, storing payment records for several years (usually required by law), and keeping usage data for a few months upto a year. Each type of data needs its own expiration date based on how long it's actually useful and what laws require.

The key is having clear rules about what to keep and what to delete. For example, Google Analytics automatically deletes old user data after 14-50 months, depending on your settings. Set similar rules for your product's data — keep what you need, delete what you don't, and be clear about these timeframes in your privacy policy.

A data taxonomy is like a dictionary for your product data — it defines what each piece of data means and how it should be named. Just like how a library organizes books by categories, your data needs a clear organization system that everyone understands.

Good names help everyone understand what the data means without having to ask around. For example, instead of "btn_clk", use "signup_button_clicked" — it's immediately clear what this event tracks. Follow a consistent pattern for all events.

Here are some best practices:

• Use the format "object_action" like "message_sent" or "profile_updated." For more detailed events, add relevant details at the end: "video_played_duration" or "article_shared_platform."
• Keep names in lowercase, use underscores between words, and avoid spaces or special characters.
• Common events should be named similarly across features. If you track button clicks, always use "_clicked" (not sometimes "_pressed" or "_tapped"). Use "started," "completed," "failed," or "updated" consistently for process stages. This makes it easier to find and compare related events across your product.
• Document these naming rules in a shared place like Notion or Confluence so all team members use the same terms when working with data.

Error tracking helps you find and fix problems in your analytics implementation. When data collection breaks, error tracking tells you exactly what went wrong and where. This is different from data validation — it focuses on technical failures like tracking code errors, API timeouts, or integration problems.

Common error tracking includes failed API calls, JavaScript errors, and data pipeline failures. Tools like Sentry and Datadog can catch these issues automatically. For example, they'll alert you if your analytics tags stop firing, if there are JavaScript errors in your tracking code, or if your data warehouse ingestion fails.

Set up alerts for different error types based on their severity. Critical errors (like complete tracking failure) need immediate attention, while minor issues (like occasional timeouts) can be reviewed daily. Group similar errors together to spot patterns — like tracking breaks after specific types of code deployments.

Documentation helps everyone understand how your analytics is set up and how to use it correctly. Good documentation is like a manual for your data — it explains what you track, why you track it, and how to use the information. Keep it simple and up-to-date.

So, basic documentation should essentially cover these 3 things:

• A list of all events and their meanings
• A guide to your naming patterns
• Instructions for common tasks

For example, document that "user_signup_completed" tracks successful account creation, while "user_signup_failed" tracks when something goes wrong during signup, with notes about what data each event includes.

Store documentation where everyone can find it easily, like Notion, Confluence, or a shared Google Doc. Include practical examples — if you track "purchase_completed," show a real event with its attributes. Update docs whenever you add new tracking or change existing events to avoid confusion.