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Track L: Data & Business Intelligence

Priority: MEDIUM — Analytics for growth decisions Agent: data-pipeline-specialist, data-warehouse-architect Sprint Range: S7-S9 Reference: docs/operations/63-data-architecture.md (22 tables, L0-L4 classification)

Status Summary​

Progress: 0% (0/22 tasks)

SectionTitleStatusTasks
L.1Data Warehouse ArchitecturePending0/4
L.2ETL/ELT PipelinePending0/4
L.3BI Dashboards & ReportingPending0/5
L.4Data Governance & QualityPending0/4
L.5Regulatory Reporting AutomationPending0/5

L.1: Data Warehouse Architecture​

Sprint: S6 | Priority: P1 | Depends On: C.1, E.2 Goal: Star schema warehouse with CDC and data lake for regulatory retention

  • L.1.1: Design data warehouse schema
    • Model: star schema — fact tables (WO events, billing events, usage events) + dimensions (time, tenant, user, asset)
    • Separation: operational DB (PostgreSQL) vs. analytical DB (BigQuery)
    • Classification: data classification mapping from L0-L4 per doc 63
  • L.1.2: Implement CDC (Change Data Capture)
    • Source: PostgreSQL logical replication to warehouse
    • Tool: Debezium or Cloud Datastream
    • Schema evolution: handling for upstream changes
  • L.1.3: Build data lake for raw events
    • Storage: GCS bucket with partitioned Parquet files
    • Layers: raw → cleansed → curated architecture
    • Retention: 24 months hot, 7 years cold (regulatory)
  • L.1.4: Create data catalog
    • Documentation: table/column documentation with business glossary
    • Lineage: tracking (source → transformation → destination)
    • Access control: per classification level (L0-L4)

L.2: ETL/ELT Pipeline​

Sprint: S7-S8 | Priority: P1 | Depends On: L.1 Goal: Automated data pipelines with quality checks and orchestration

  • L.2.1: Build core data pipelines
    • WO lifecycle: raw events → dimensional model
    • User activity: login, feature usage → engagement metrics
    • Financial: subscriptions, invoices → revenue metrics
  • L.2.2: Implement data transformation layer
    • Tool: dbt (data build tool) for SQL transformations
    • Materialized views: for common queries
    • Incremental processing: for efficiency
  • L.2.3: Create pipeline orchestration
    • Tool: Cloud Composer (Airflow) or Cloud Workflows
    • Schedule: real-time for events, hourly for aggregates, daily for reports
    • Alerting: pipeline failures with auto-retry
  • L.2.4: Build data quality checks
    • Validation: row count, null checks, referential integrity
    • Freshness: stale data alerting
    • Contracts: Great Expectations or dbt tests for data contracts

L.3: BI Dashboards & Reporting​

Sprint: S8 | Priority: P1 | Depends On: L.1, L.2 Goal: Executive, operational, compliance, and customer-facing dashboards

  • L.3.1: Build executive dashboard
    • Metrics: ARR, customer count, NRR, logo churn, gross margin
    • Trends: monthly, quarterly, YoY
    • Segments: by tier, ICP, region
  • L.3.2: Create operational dashboard
    • WO metrics: throughput, cycle time, SLA compliance
    • AI metrics: agent autonomy rate, token consumption
    • Support: ticket volume, resolution time
  • L.3.3: Build compliance dashboard
    • Readiness: audit readiness score per tenant
    • Open items: CAPAs, overdue deviations, training compliance %
    • Coverage: regulatory framework heat map
  • L.3.4: Create customer-facing analytics
    • Embedded: analytics in customer portal
    • WO performance: per organization
    • Compliance: metrics and trend reports
  • L.3.5: Implement report scheduling and distribution
    • Delivery: automated email (daily, weekly, monthly)
    • Format: PDF report generation
    • Custom: report builder for power users

L.4: Data Governance & Quality​

Sprint: S8-S9 | Priority: P1 | Depends On: L.1 Goal: Role-based access, quality monitoring, privacy compliance, and retention automation

  • L.4.1: Implement data access governance
    • RBAC: role-based access to warehouse tables
    • Column-level: security for L3/L4 data
    • Audit: access audit logging
  • L.4.2: Build data quality monitoring
    • Scoring: automated data quality scoring per table
    • Anomaly detection: on key metrics
    • SLA: data quality SLA and reporting
  • L.4.3: Create data privacy compliance
    • PII inventory: mapping across all tables
    • Right-to-erasure: implementation in warehouse
    • Cross-border: data transfer tracking
  • L.4.4: Implement data retention automation
    • Policies: per-regulation retention (Part 11: 7yr, HIPAA: 6yr per doc 63)
    • Archival: automated to cold storage
    • Reporting: retention compliance reporting

L.5: Regulatory Reporting Automation​

Sprint: S9 | Priority: P2 | Depends On: L.2, L.3 Goal: Automated evidence packages for FDA, HIPAA, and SOC 2 audits

  • L.5.1: Build FDA submission data package
    • CSV evidence: Computer System Validation export
    • Audit trail: extracts (date range, user, action filters)
    • Validation: IQ/OQ/PQ test execution evidence
  • L.5.2: Create HIPAA reporting suite
    • Access reports: frequency per PHI field
    • Anomaly: unusual access pattern detection
    • Breach: notification data preparation
  • L.5.3: Build SOC 2 evidence automation
    • Controls: effectiveness reports per Trust Service Criteria
    • Snapshots: monthly evidence with tamper detection
    • Auditor portal: read-only, time-limited access
  • L.5.4: Create audit readiness dashboard
    • Score: real-time audit readiness score per framework (FDA, HIPAA, SOC 2)
    • Evidence gaps: automated identification of missing or stale evidence
    • Timeline: audit preparation timeline with task assignments
    • Historical: past audit findings tracking and remediation status
  • L.5.5: Implement regulatory submission automation
    • Packaging: automated evidence package assembly per submission type
    • Formatting: agency-specific format requirements (FDA eCTD, EMA NeeS)
    • Tracking: submission tracking with agency response monitoring
    • Templates: configurable submission templates per regulatory pathway

Updated: 2026-02-14 Compliance: CODITECT Track Nomenclature Standard (ADR-054)