Analytics & Reporting Service

Comprehensive analytics platform for tracking learner progress, engagement, and business intelligence

Requirements Document

Comprehensive requirements for the Analytics & Reporting Service in the KnowledgeTracker Platform.

1. Functional Requirements

1.1 Learner Analytics

  • •Individual learner progress tracking across all courses
  • •Time spent on learning activities and content
  • •Assessment scores and performance trends
  • •Skill acquisition and competency mapping
  • •Learning path completion rates

1.2 Course Analytics

  • •Enrollment and completion rates per course
  • •Content engagement metrics (video watch time, resource downloads)
  • •Drop-off points and friction analysis
  • •Assessment difficulty and pass rates
  • •Course ratings and feedback aggregation

1.3 Engagement Metrics

  • •Daily/weekly/monthly active users (DAU/WAU/MAU)
  • •Session duration and frequency
  • •Feature usage statistics (forums, live classes, certificates)
  • •User retention and churn analysis
  • •Cohort analysis for user groups

1.4 Business Intelligence

  • •Revenue analytics by course, instructor, and period
  • •User acquisition and conversion funnels
  • •Customer lifetime value (CLV) calculations
  • •Marketing campaign ROI tracking
  • •Subscription growth and churn metrics

1.5 Custom Reports & Dashboards

  • •Drag-and-drop dashboard builder
  • •Pre-built dashboard templates for common use cases
  • •Custom report creation with filters and dimensions
  • •Scheduled report delivery via email
  • •Export to PDF, Excel, CSV formats

1.6 Real-Time Analytics

  • •Live user activity monitoring
  • •Real-time enrollment and completion tracking
  • •System performance and health metrics
  • •Alert triggers for anomalies or thresholds

2. Non-Functional Requirements

2.1 Performance

  • •Dashboard load time < 2 seconds for standard reports
  • •Real-time data refresh every 30 seconds
  • •Support for analyzing 100M+ data points

2.2 Scalability

  • •Data warehouse architecture for historical data
  • •Columnar storage for fast aggregations
  • •Data partitioning by time and organization

2.3 Data Quality

  • •Data validation and cleansing pipelines
  • •99.9% data accuracy for reported metrics
  • •Audit trails for data transformations

2.4 Security & Privacy

  • •Role-based access control for reports and dashboards
  • •Data anonymization for learner privacy
  • •Multi-tenant data isolation
  • •GDPR-compliant data retention policies

Requirements Validation

Use this requirements document alongside the Database Design to validate:

  • ✓All analytics metrics can be calculated from available data
  • ✓Event tracking schema supports required dimensions
  • ✓Real-time and historical data pipelines are architecturally sound
  • ✓Multi-tenant reporting isolation is properly implemented