Finvu Data Warehouse Documentation

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Current Pain Points Analysis

⚠️

Critical Business Challenges

Analysis of operational pain points impacting business growth and customer success

🔍 Strategic Analysis Framework

Step 1: Request Analysis

📋 Business Requirements Assessment

Customer Success Team
  • • Root cause analysis capabilities
  • • Funnel drop-off identification
  • • <3 second drill-down requirement
  • • FIP vs AA performance attribution
Business Team
  • • Revenue attribution by purpose code
  • • Growth rate tracking (2x market target)
  • • Client performance comparison
  • • Market share progression monitoring
Regulatory Team
  • • Automated Sahamati reporting
  • • RBI compliance tracking
  • • Audit trail maintenance (7+ years)
  • • FI type & license categorization
Tech Team
  • • Real-time performance monitoring
  • • Event-level debugging capabilities
  • • Infrastructure health attribution
  • • API performance analytics

Step 2: Current State Assessment

⚙️ Current Infrastructure Challenges

Data Trust Issues

Core Problem: “Data given by us is always under inspection of being right or wrong”

Impact: Business teams spend significant time validating data accuracy instead of focusing on insights and action

Resource Constraints

Core Problem: “Assets often insufficient for new requirements”

Impact: Delays in delivering new analytics capabilities and reactive approach to business needs

Step 3: Operational Pain Points

🔧 Implementation Examples

Example 1: Consent POST API Optimization

Scenario: “We started retrying on consent POST API before sending notification as failed to FIUs”

❌ Previous Approach
  • • Immediate failure notification to FIUs
  • • No retry mechanism
  • • Higher perceived failure rates
  • • Client dissatisfaction
✅ Improved Approach
  • • Retry logic before failure notification
  • • Improved success rates
  • • Better client experience
  • • Reduced false negative alerts

📊 Root Cause Analysis

🔍 Systemic Issues Identification

Data Quality Issues

  • • Inconsistent event schema evolution
  • • Missing lineage tracking
  • • Manual data validation processes
  • • Lack of automated quality checks

Performance Challenges

  • • Query latency exceeding <3s requirement
  • • Limited real-time capabilities
  • • Inefficient drill-down paths
  • • Resource contention during peak loads

Scalability Constraints

  • • Ad-hoc report generation approach
  • • Manual compliance reporting
  • • Limited multi-dimensional analysis
  • • Reactive development model

🎯 Impact Assessment

📈 Business Impact Quantification

Revenue Impact

Customer Success Response Time+2-3 days
Business Decision Latency+1-2 weeks
Client Trust Score↓ Medium Risk

Operational Efficiency

Manual Reporting Effort40-60% of time
Data Validation Time25-30% of analysis
Ad-hoc Request Queue2-4 weeks backlog

💡 Strategic Implications

🎯 Priority Actions Required

Immediate (Next 30 days)

  • Implement automated data quality monitoring

  • Establish bronze layer for reliable data ingestion

  • Deploy star schema for <3s drill-down capability

Medium-term (Next 90 days)

  • Automate Sahamati & RBI reporting

  • Build multi-dimensional analysis capabilities

  • Implement predictive customer success alerts