mirror of
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Expand to Business Analytics: Add Customer, Sales, and KPI models
Major expansion from fraud detection to comprehensive business analytics: DATABASE CHANGES: - Renamed database from 'fraud_detection' to 'business_analytics' - Renamed user from 'fraud_analyst' to 'data_analyst' - Expanded from 20 to 39 tables across 4 business models NEW MODELS (19 tables): 1. Customer Analytics (5 tables): - customer_segments, customer_lifetime_value, churn_predictions - customer_satisfaction, engagement_metrics 2. Sales & Revenue Analytics (6 tables): - product_catalog, sales_transactions, sales_targets - sales_performance, revenue_forecasts 3. KPI & Metrics (8 tables): - kpi_definitions, daily_metrics, monthly_summaries - trend_analysis, dashboard_snapshots - report_definitions, report_executions, data_quality_checks DATA GENERATION: - Extended generate_data.sh with 6 new steps (now 15 total) - Added CLV calculations for all customers - Added churn predictions based on transaction recency - Added 30K customer satisfaction surveys - Added 1M sales transactions linked to 24 products - Added 90 days of daily KPI metrics - Added 24 months of business summaries SQL EXERCISES (3 new levels): - Level 2: Customer Analytics (10 exercises + 3 challenges) - Level 3: Sales & Revenue Analysis (12 exercises + 3 challenges) - Level 4: KPI Dashboards & Metrics (12 exercises + 3 challenges) DOCUMENTATION: - Updated README.md with business analytics focus - Updated QUICKSTART.md with new data generation steps - Updated SETUP_COMPLETE.md with 39-table architecture - Added DATA_MODELS.md with complete model specifications - Added WHATS_NEW.md with migration guide SEED DATA: - Added 8 customer segments (VIP, High Value, etc.) - Added 24 products across 5 categories - Added 16 KPI definitions across 4 categories - Added 8 standard report definitions All changes maintain idempotency and backward compatibility with existing fraud detection functionality.
This commit is contained in:
Alphaeus Mote
+270
-16
@@ -1,9 +1,9 @@
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#!/bin/bash
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# ============================================================================
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# Financial Fraud Detection - Data Generation Script - IDEMPOTENT
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# Business Analytics - Data Generation Script - IDEMPOTENT
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# ============================================================================
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# Generates realistic test data with embedded fraud patterns
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# Generates realistic test data for business analytics and reporting
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# This script is IDEMPOTENT - it will clear and regenerate all data
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# ============================================================================
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@@ -19,8 +19,8 @@ NC='\033[0m' # No Color
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# Configuration
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DB_HOST="${POSTGRES_HOST:-localhost}"
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DB_PORT="${POSTGRES_PORT:-5432}"
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DB_NAME="${POSTGRES_DB:-fraud_detection}"
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DB_USER="${POSTGRES_USER:-fraud_analyst}"
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DB_NAME="${POSTGRES_DB:-business_analytics}"
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DB_USER="${POSTGRES_USER:-data_analyst}"
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DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
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# Data volumes
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@@ -33,7 +33,7 @@ NUM_TRANSACTIONS=5000000
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FRAUD_PERCENTAGE=7 # 7% of transactions will be fraudulent
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "${BLUE}Financial Fraud Detection Database - Data Generation${NC}"
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echo -e "${BLUE}Business Analytics Database - Data Generation${NC}"
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "Target Database: ${GREEN}$DB_NAME@$DB_HOST:$DB_PORT${NC}"
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echo -e "Customers: ${GREEN}$NUM_CUSTOMERS${NC}"
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@@ -62,7 +62,7 @@ execute_sql_file() {
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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# Clear existing data (preserve reference tables)
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echo -e "${YELLOW}[0/9] Clearing existing data...${NC}"
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echo -e "${YELLOW}[0/15] Clearing existing data...${NC}"
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execute_sql "TRUNCATE TABLE audit_log CASCADE;"
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execute_sql "TRUNCATE TABLE suspicious_activity_reports CASCADE;"
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execute_sql "TRUNCATE TABLE case_alerts CASCADE;"
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@@ -83,12 +83,12 @@ echo -e "${GREEN}✓ Existing data cleared${NC}"
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echo ""
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# Load geographic reference data
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echo -e "${YELLOW}[1/9] Loading geographic reference data...${NC}"
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echo -e "${YELLOW}[1/15] Loading geographic reference data...${NC}"
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execute_sql_file "$SCRIPT_DIR/reference/load_geographic_data.sql" > /dev/null
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echo -e "${GREEN}✓ Geographic data loaded (100 US cities, 210 world cities)${NC}"
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echo ""
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echo -e "${YELLOW}[2/9] Generating Customers...${NC}"
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echo -e "${YELLOW}[2/15] Generating Customers...${NC}"
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cat > /tmp/generate_customers.sql << 'EOF'
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-- Generate customers with realistic geographic data
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INSERT INTO customers (
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@@ -130,7 +130,7 @@ execute_sql_file /tmp/generate_customers.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_CUSTOMERS customers${NC}"
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echo ""
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echo -e "${YELLOW}[3/9] Generating Accounts...${NC}"
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echo -e "${YELLOW}[3/15] Generating Accounts...${NC}"
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cat > /tmp/generate_accounts.sql << 'EOF'
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-- Generate accounts (1-2 accounts per customer on average)
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INSERT INTO accounts (
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@@ -177,7 +177,7 @@ execute_sql_file /tmp/generate_accounts.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_ACCOUNTS accounts${NC}"
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echo ""
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echo -e "${YELLOW}[4/9] Generating Merchants...${NC}"
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echo -e "${YELLOW}[4/15] Generating Merchants...${NC}"
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cat > /tmp/generate_merchants.sql << 'EOF'
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-- Generate merchants with realistic geographic data
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INSERT INTO merchants (
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@@ -237,7 +237,7 @@ execute_sql_file /tmp/generate_merchants.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_MERCHANTS merchants${NC}"
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echo ""
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echo -e "${YELLOW}[5/9] Generating Devices...${NC}"
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echo -e "${YELLOW}[5/15] Generating Devices...${NC}"
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cat > /tmp/generate_devices.sql << 'EOF'
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-- Generate devices
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INSERT INTO devices (
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@@ -282,7 +282,7 @@ execute_sql_file /tmp/generate_devices.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_DEVICES devices${NC}"
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echo ""
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echo -e "${YELLOW}[6/9] Generating Cards...${NC}"
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echo -e "${YELLOW}[6/15] Generating Cards...${NC}"
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cat > /tmp/generate_cards.sql << 'EOF'
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-- Generate cards (1-2 cards per account on average)
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INSERT INTO cards (
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@@ -326,7 +326,7 @@ execute_sql_file /tmp/generate_cards.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_CARDS cards${NC}"
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echo ""
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echo -e "${YELLOW}[7/9] Generating Login Sessions...${NC}"
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echo -e "${YELLOW}[7/15] Generating Login Sessions...${NC}"
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cat > /tmp/generate_sessions.sql << 'EOF'
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-- Generate login sessions with realistic geographic data
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INSERT INTO login_sessions (
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@@ -359,7 +359,7 @@ execute_sql_file /tmp/generate_sessions.sql > /dev/null
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echo -e "${GREEN}✓ Generated 500,000 login sessions${NC}"
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echo ""
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echo -e "${YELLOW}[8/9] Generating Transactions (this may take a while)...${NC}"
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echo -e "${YELLOW}[8/15] Generating Transactions (this may take a while)...${NC}"
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echo -e "${BLUE}This step generates $NUM_TRANSACTIONS transactions with fraud patterns${NC}"
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# Generate transactions in batches to avoid memory issues
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@@ -433,7 +433,7 @@ done
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echo -e "${GREEN}✓ Generated $NUM_TRANSACTIONS transactions${NC}"
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echo ""
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echo -e "${YELLOW}[9/9] Generating Fraud Cases and Alerts...${NC}"
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echo -e "${YELLOW}[9/15] Generating Fraud Cases and Alerts...${NC}"
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# Generate alerts for flagged transactions
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execute_sql "
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@@ -535,7 +535,261 @@ case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;" | grep -E '^\s*[0-9
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echo -e "Fraud Cases: ${GREEN}$case_count${NC}"
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echo ""
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echo -e "${YELLOW}Ready for SQL learning and fraud investigation!${NC}"
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "${BLUE}Generating Analytics Data (Customer, Sales, KPIs)${NC}"
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echo -e "${BLUE}============================================================================${NC}"
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echo ""
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# ============================================================================
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# GENERATE CUSTOMER ANALYTICS DATA
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# ============================================================================
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echo -e "${YELLOW}[10/15] Generating Customer Lifetime Value data...${NC}"
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cat > /tmp/generate_clv.sql << 'EOF'
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-- Generate CLV for all customers based on their transaction history
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INSERT INTO customer_lifetime_value (
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customer_id, calculation_date, total_revenue, total_transactions,
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average_order_value, predicted_future_value, clv_score, segment_id
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)
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SELECT
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c.customer_id,
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CURRENT_DATE as calculation_date,
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COALESCE(SUM(t.amount), 0) as total_revenue,
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COUNT(t.transaction_id) as total_transactions,
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COALESCE(AVG(t.amount), 0) as average_order_value,
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COALESCE(SUM(t.amount) * 1.5, 0) as predicted_future_value,
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COALESCE(SUM(t.amount) / 100, 0) as clv_score,
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CASE
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WHEN COALESCE(SUM(t.amount), 0) >= 50000 THEN 1 -- VIP
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WHEN COALESCE(SUM(t.amount), 0) >= 10000 THEN 2 -- High Value
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WHEN COALESCE(SUM(t.amount), 0) >= 2000 THEN 3 -- Medium Value
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WHEN COALESCE(SUM(t.amount), 0) >= 500 THEN 4 -- Low Value
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ELSE 6 -- New Customer
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END as segment_id
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FROM customers c
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LEFT JOIN accounts a ON c.customer_id = a.customer_id
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LEFT JOIN transactions t ON a.account_id = t.account_id
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GROUP BY c.customer_id;
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EOF
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execute_sql_file /tmp/generate_clv.sql > /dev/null
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echo -e "${GREEN}✓ Generated CLV for all customers${NC}"
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echo ""
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echo -e "${YELLOW}[11/15] Generating Churn Predictions...${NC}"
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cat > /tmp/generate_churn.sql << 'EOF'
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-- Generate churn predictions based on transaction recency
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INSERT INTO churn_predictions (
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customer_id, prediction_date, churn_probability, risk_level,
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last_transaction_date, days_since_last_transaction, engagement_score
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)
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SELECT
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c.customer_id,
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CURRENT_DATE as prediction_date,
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CASE
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WHEN MAX(t.transaction_date) IS NULL THEN 90
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 85
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 60
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 30
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ELSE 10
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END as churn_probability,
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CASE
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WHEN MAX(t.transaction_date) IS NULL THEN 'HIGH'
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 'CRITICAL'
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 'HIGH'
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 'MEDIUM'
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ELSE 'LOW'
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END as risk_level,
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MAX(t.transaction_date) as last_transaction_date,
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COALESCE(CURRENT_DATE - MAX(t.transaction_date), 999) as days_since_last_transaction,
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CASE
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WHEN MAX(t.transaction_date) IS NULL THEN 0
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 10
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 30
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WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 60
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ELSE 90
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END as engagement_score
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FROM customers c
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LEFT JOIN accounts a ON c.customer_id = a.customer_id
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LEFT JOIN transactions t ON a.account_id = t.account_id
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GROUP BY c.customer_id;
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EOF
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execute_sql_file /tmp/generate_churn.sql > /dev/null
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echo -e "${GREEN}✓ Generated churn predictions${NC}"
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echo ""
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echo -e "${YELLOW}[12/15] Generating Customer Satisfaction data...${NC}"
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cat > /tmp/generate_satisfaction.sql << 'EOF'
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-- Generate satisfaction scores for random sample of customers
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INSERT INTO customer_satisfaction (
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customer_id, survey_date, nps_score, csat_score, category, sentiment
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)
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SELECT
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customer_id,
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TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' as survey_date,
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(random() * 200 - 100)::INT as nps_score,
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(1 + random() * 4)::DECIMAL(3,2) as csat_score,
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CASE (random() * 5)::INT
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WHEN 0 THEN 'PRODUCT'
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WHEN 1 THEN 'SERVICE'
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WHEN 2 THEN 'SUPPORT'
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WHEN 3 THEN 'BILLING'
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ELSE 'OTHER'
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END as category,
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CASE
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WHEN random() < 0.6 THEN 'POSITIVE'
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WHEN random() < 0.85 THEN 'NEUTRAL'
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ELSE 'NEGATIVE'
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END as sentiment
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FROM customers
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WHERE random() < 0.3 -- 30% of customers have satisfaction data
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LIMIT 30000;
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EOF
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execute_sql_file /tmp/generate_satisfaction.sql > /dev/null
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echo -e "${GREEN}✓ Generated ~30,000 satisfaction records${NC}"
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echo ""
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# ============================================================================
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# GENERATE SALES ANALYTICS DATA
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# ============================================================================
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echo -e "${YELLOW}[13/15] Generating Sales Transactions...${NC}"
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cat > /tmp/generate_sales.sql << 'EOF'
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-- Link transactions to products
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INSERT INTO sales_transactions (
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transaction_id, product_id, quantity, unit_price, discount_amount,
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tax_amount, total_amount, sale_date, sales_channel, region
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)
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SELECT
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t.transaction_id,
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((t.transaction_id % 24) + 1) as product_id, -- Cycle through 24 products
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(1 + (random() * 3)::INT) as quantity,
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t.amount / (1 + (random() * 3)::INT) as unit_price,
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CASE WHEN random() < 0.2 THEN t.amount * 0.1 ELSE 0 END as discount_amount,
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t.amount * 0.08 as tax_amount,
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t.amount as total_amount,
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t.transaction_date as sale_date,
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CASE (t.transaction_id % 4)
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WHEN 0 THEN 'ONLINE'
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WHEN 1 THEN 'STORE'
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WHEN 2 THEN 'PHONE'
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ELSE 'MOBILE_APP'
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END as sales_channel,
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CASE (t.transaction_id % 5)
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WHEN 0 THEN 'Northeast'
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WHEN 1 THEN 'Southeast'
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WHEN 2 THEN 'Midwest'
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WHEN 3 THEN 'Southwest'
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ELSE 'West'
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END as region
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FROM transactions t
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WHERE t.status = 'COMPLETED'
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LIMIT 1000000; -- Link 1M transactions to products
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EOF
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execute_sql_file /tmp/generate_sales.sql > /dev/null
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echo -e "${GREEN}✓ Generated 1,000,000 sales transaction records${NC}"
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echo ""
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# ============================================================================
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# GENERATE KPI & METRICS DATA
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# ============================================================================
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echo -e "${YELLOW}[14/15] Generating Daily Metrics...${NC}"
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cat > /tmp/generate_metrics.sql << 'EOF'
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-- Generate daily metrics for the past 90 days
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INSERT INTO daily_metrics (
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metric_date, kpi_id, metric_value, vs_previous_day_percentage, status
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)
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SELECT
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date_series.metric_date,
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kpi.kpi_id,
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kpi.target_value * (0.8 + random() * 0.4) as metric_value,
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(-20 + random() * 40)::DECIMAL(5,2) as vs_previous_day_percentage,
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CASE
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WHEN random() < 0.7 THEN 'ON_TARGET'
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WHEN random() < 0.9 THEN 'WARNING'
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ELSE 'CRITICAL'
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END as status
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FROM generate_series(
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CURRENT_DATE - INTERVAL '90 days',
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CURRENT_DATE,
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INTERVAL '1 day'
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) AS date_series(metric_date)
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CROSS JOIN kpi_definitions kpi
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WHERE kpi.is_active = TRUE;
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EOF
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execute_sql_file /tmp/generate_metrics.sql > /dev/null
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echo -e "${GREEN}✓ Generated 90 days of daily metrics${NC}"
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echo ""
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echo -e "${YELLOW}[15/15] Generating Monthly Summaries...${NC}"
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cat > /tmp/generate_monthly.sql << 'EOF'
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-- Generate monthly summaries for the past 24 months
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INSERT INTO monthly_summaries (
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summary_month, summary_year, total_revenue, total_transactions,
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total_customers, new_customers, average_transaction_value
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)
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SELECT
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EXTRACT(MONTH FROM month_series)::INT as summary_month,
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EXTRACT(YEAR FROM month_series)::INT as summary_year,
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(10000000 + random() * 5000000)::DECIMAL(15,2) as total_revenue,
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(50000 + (random() * 30000)::INT) as total_transactions,
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(80000 + (random() * 20000)::INT) as total_customers,
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(500 + (random() * 1500)::INT) as new_customers,
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(100 + random() * 100)::DECIMAL(15,2) as average_transaction_value
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FROM generate_series(
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CURRENT_DATE - INTERVAL '24 months',
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CURRENT_DATE,
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INTERVAL '1 month'
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) AS month_series;
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EOF
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execute_sql_file /tmp/generate_monthly.sql > /dev/null
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echo -e "${GREEN}✓ Generated 24 months of summaries${NC}"
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echo ""
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "${GREEN}Data Generation Complete!${NC}"
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echo -e "${BLUE}============================================================================${NC}"
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echo ""
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echo -e "${YELLOW}Database Statistics:${NC}"
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customer_count=$(execute_sql "SELECT COUNT(*) FROM customers;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Customers: ${GREEN}$customer_count${NC}"
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account_count=$(execute_sql "SELECT COUNT(*) FROM accounts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Accounts: ${GREEN}$account_count${NC}"
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merchant_count=$(execute_sql "SELECT COUNT(*) FROM merchants;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Merchants: ${GREEN}$merchant_count${NC}"
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card_count=$(execute_sql "SELECT COUNT(*) FROM cards;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Cards: ${GREEN}$card_count${NC}"
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transaction_count=$(execute_sql "SELECT COUNT(*) FROM transactions;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Transactions: ${GREEN}$transaction_count${NC}"
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alert_count=$(execute_sql "SELECT COUNT(*) FROM alerts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Alerts: ${GREEN}$alert_count${NC}"
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case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Fraud Cases: ${GREEN}$case_count${NC}"
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clv_count=$(execute_sql "SELECT COUNT(*) FROM customer_lifetime_value;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Customer CLV Records: ${GREEN}$clv_count${NC}"
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sales_count=$(execute_sql "SELECT COUNT(*) FROM sales_transactions;" | grep -E '^\s*[0-9]+' | tr -d ' ')
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echo -e "Sales Records: ${GREEN}$sales_count${NC}"
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metrics_count=$(execute_sql "SELECT COUNT(*) FROM daily_metrics;" | grep -E '^\s*[0-9]+' | tr -d ' ')
|
||||
echo -e "Daily Metrics: ${GREEN}$metrics_count${NC}"
|
||||
|
||||
echo ""
|
||||
echo -e "${YELLOW}Ready for Business Analytics and SQL learning!${NC}"
|
||||
echo -e "Access DB-UI at: ${BLUE}http://localhost:3000${NC}"
|
||||
echo ""
|
||||
|
||||
|
||||
Reference in New Issue
Block a user