Archiving Crowd-Generated Events

Crowd Analytics currently does not support a native "archive" feature for events. This article explains why this limitation exists and provides practical solutions for managing unwanted or outdated events in your analytics setup.

Current Archiving Status

Archive Feature: Not Currently Supported

At this time, Crowd does not offer a built in archiving functionality for tracked events. Once events are recorded in your analytics, they cannot be archived, hidden, or removed through the Crowd dashboard interface.

Why Archiving Isn't Available

Crowd maintains a philosophy of data integrity and transparency by avoiding post-processing or masking of recorded events. This approach ensures:

• Data Accuracy: All recorded events remain untouched and authentic

• Reporting Clarity: Analytics reflect actual user interactions without artificial modifications

• System Simplicity: Streamlined data pipeline without complex filtering layers

Recommended Solutions

Primary Method: Remove at Source

The most effective way to stop tracking unwanted events is to remove them from your application code:

1. Locate the Event Code: Find the crowd.track(event_name, data) call in your codebase

2. Remove or Comment Out: Delete or comment out the specific tracking code

3. Deploy Changes: Push your updated code to production

Example:

// Remove this line to stop tracking
// crowd.track('old_feature_click', { feature: 'deprecated_button' });

Alternative Approaches

For Active Management:

• Use custom filters in your reporting UI to exclude specific events

• Implement client-side conditional logic to stop tracking based on feature flags

• Create separate data pipelines that filter out unwanted events

For Historical Data:

• Historical event data will remain in your analytics

• New instances of removed events will stop appearing

• Existing data won't grow or affect new analytics calculations

Best Practices

Before Removing Events

1. Document the Decision: Record why you're removing specific events

2. Export Historical Data: Save important historical data if needed for future reference

3. Review Dependencies: Ensure other team members aren't relying on the event data

Code Management

1. Version Control: Use proper git commits when removing tracking code

2. Code Comments: Leave comments explaining removed events for future developers

3. Gradual Removal: Consider phasing out events gradually rather than all at once

Frequently Asked Questions

Will removed events affect my existing reports?

No, removing event tracking code only stops new data collection. Historical data remains intact and accessible in your existing reports.

Can I temporarily disable event tracking?

Yes, you can use feature flags or conditional statements to temporarily stop tracking specific events without permanently removing the code.

What if I accidentally remove important tracking?

You can restore the tracking code from your version control system. However, you'll lose data for the period when tracking was disabled.

Future Considerations

Planned Features

Currently, Crowd has no immediate plans to implement native archiving functionality. The development team prioritizes maintaining data integrity over post-processing capabilities.

Feedback Welcome

If event archiving is crucial to your workflow, the Crowd team welcomes feedback about your specific use cases. This input helps inform future feature development decisions.

Getting Additional Help

When to Contact Support

• Need help identifying which events to remove

• Experiencing technical difficulties removing tracking code

• Require assistance with custom filtering solutions

Self-Service Resources

• Review your current event tracking implementation

• Use your analytics dashboard to identify low-value events

• Consult your development team about safe code removal practices

While Crowd Analytics doesn't support native event archiving, removing unwanted events at the source code level provides an effective solution. This approach maintains data integrity while giving you control over future data collection. Remember that historical data remains preserved, ensuring you don't lose valuable insights from past user interactions.