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.
2026-07-26 11:28:16 +00:00 · 2025-10-23 14:17:12 -04:00
parent b30733ccad
commit aa803bd3bd
14 changed files with 2762 additions and 132 deletions
@@ -1,44 +1,82 @@
-# Financial Fraud Detection - SQL Learning Database
+# Business Analytics - SQL Learning Database
-A comprehensive, production-grade database designed for learning SQL through realistic financial fraud investigation scenarios.
+A comprehensive, production-grade database designed for learning SQL through realistic business analytics, customer insights, sales analysis, and fraud detection scenarios.
 ## 🎯 Overview
-This project provides a complete PostgreSQL database with **5+ million transactions**, embedded fraud patterns, and progressive SQL exercises. Perfect for:
+This project provides a complete PostgreSQL database with **5+ million transactions**, **39 tables** across 4 business models, and progressive SQL exercises aligned with **Data Analyst** responsibilities. Perfect for:
 - **SQL Beginners** → Learn fundamentals with real-world data
- **Data Analysts** → Practice fraud detection queries
+- **Data Analysts** → Practice customer analytics, sales analysis, KPI dashboards
- **Security Professionals** → Understand fraud patterns
+- **Business Analysts** → Understand customer behavior and revenue patterns
- **Students** → Hands-on financial crime investigation
+- **Security Professionals** → Detect fraud patterns
 - **Students** → Hands-on business intelligence and analytics
 ## 📊 Database Statistics
 ### Core Data
 - **100,000** Customers with KYC data
 - **150,000** Bank accounts (checking, savings, credit)
 - **200,000** Payment cards
 - **50,000** Merchants across 35 categories
 - **5,000,000** Transactions (7% fraudulent)
 - **1,000,000** Sales records linked to products
 - **500,000** Login sessions
 ### Analytics Data
 - **100,000** Customer Lifetime Value calculations
 - **100,000** Churn predictions
 - **30,000** Customer satisfaction surveys
 - **24** Product catalog entries
 - **1,500+** Daily KPI metrics
 - **24** Monthly business summaries
 - **50,000+** Fraud alerts
 - **5,000+** Fraud cases
 - **500,000** Login sessions
 ## 🏗️ Architecture
 ### Data Model Features
- ✅ **20+ Tables** with proper relationships
+- ✅ **39 Tables** across 4 business models
 - ✅ **Foreign key constraints** for data integrity
 - ✅ **Indexes** for query performance
 - ✅ **Realistic geographic data** (100 US cities, 210 world cities)
- ✅ **Embedded fraud patterns** (velocity, geographic, structuring)
+- ✅ **Customer analytics** (CLV, churn, satisfaction, engagement)
 - ✅ **Sales analytics** (products, targets, forecasts)
 - ✅ **KPI tracking** (daily metrics, trends, dashboards)
 - ✅ **Fraud detection** (velocity, geographic, structuring patterns)
 - ✅ **Audit trails** and compliance tables
-### Key Entities
+### Business Models
 #### 1. Fraud Detection Model (20 tables)
 ```
-customers → accounts → transactions
+customers → accounts → transactions → alerts → fraud_cases
-                    ↓
+              ↓
-                  cards → merchants
+            cards → merchants
-                    ↓
+```
-                 alerts → fraud_cases
+
 #### 2. Customer Analytics Model (5 tables)
 ```
 customers → customer_lifetime_value → customer_segments
         → churn_predictions
         → customer_satisfaction
         → engagement_metrics
 ```
 #### 3. Sales & Revenue Model (6 tables)
 ```
 product_catalog → sales_transactions → sales_performance
                                    → sales_targets
                                    → revenue_forecasts
 ```
 #### 4. KPI & Metrics Model (8 tables)
 ```
 kpi_definitions → daily_metrics → trend_analysis
               → monthly_summaries
               → dashboard_snapshots
               → report_definitions → report_executions
               → data_quality_checks
 ```
 ## 🚀 Quick Start
@@ -86,15 +124,15 @@ http://localhost:3000
 **Option B: Command Line**
 ```bash
-docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
+docker exec -it business_analytics_db psql -U data_analyst -d business_analytics
 ```
 **Option C: Your Favorite SQL Client**
 ```
 Host: localhost
 Port: 5432
-Database: fraud_detection
+Database: business_analytics
-Username: fraud_analyst
+Username: data_analyst
 Password: SecurePass123!
 ```
@@ -106,29 +144,36 @@ Password: SecurePass123!
 - Basic comparisons
 - **Location:** `exercises/01-basic-queries/`
-### Level 2: Joins
+### Level 2: Customer Analytics ⭐ NEW
- INNER JOIN, LEFT JOIN
+- Customer segmentation
- Multiple table queries
+- Lifetime value (CLV) analysis
- Relationship navigation
+- Churn prediction
- **Location:** `exercises/02-joins/`
+- Satisfaction metrics
 - Engagement tracking
 - **Location:** `exercises/02-customer-analytics/`
-### Level 3: Aggregations
+### Level 3: Sales & Revenue Analysis ⭐ NEW
- COUNT, SUM, AVG, MAX, MIN
+- Sales performance vs targets
- GROUP BY and HAVING
+- Product analytics
- Statistical analysis
+- Channel and regional analysis
- **Location:** `exercises/03-aggregations/`
+- Revenue forecasting
 - Profitability analysis
 - **Location:** `exercises/03-sales-analysis/`
-### Level 4: Subqueries
+### Level 4: KPI Dashboards & Metrics ⭐ NEW
- Nested queries
+- KPI tracking and monitoring
- Correlated subqueries
+- Trend analysis
- EXISTS and IN
+- Dashboard creation
- **Location:** `exercises/04-subqueries/`
+- Data quality monitoring
 - Executive reporting
 - **Location:** `exercises/04-kpi-dashboards/`
-### Level 5: Window Functions
+### Level 5: Advanced SQL Techniques
- ROW_NUMBER, RANK, DENSE_RANK
+- Window functions (ROW_NUMBER, RANK)
- Running totals
+- Common Table Expressions (CTEs)
- Moving averages
+- Running totals and moving averages
- **Location:** `exercises/05-window-functions/`
+- Complex aggregations
 - **Location:** `exercises/05-advanced-sql/`
 ### Level 6: Fraud Detection
 - Velocity fraud detection
@@ -137,6 +182,40 @@ Password: SecurePass123!
 - Account takeover patterns
 - **Location:** `exercises/06-fraud-detection/`
 ## 💼 Data Analyst Use Cases
 This database supports typical **Data Analyst** responsibilities:
 ### Routine Analysis
 - Daily sales summaries
 - Customer acquisition metrics
 - Transaction volume tracking
 - Basic KPI monitoring
 ### Semi-Routine Reporting
 - Weekly customer analytics
 - Monthly revenue reports
 - Product performance analysis
 - Churn risk identification
 ### Dashboard Creation
 - Executive KPI dashboards
 - Sales performance dashboards
 - Customer health dashboards
 - Operational metrics dashboards
 ### Trend Identification
 - Revenue trends (MoM, YoY)
 - Customer behavior patterns
 - Product sales seasonality
 - Engagement score trends
 ### Data Quality
 - Missing data detection
 - Anomaly identification
 - Validation checks
 - Data completeness monitoring
 ## 🔍 Fraud Patterns Included
 ### 1. Velocity Fraud
@@ -180,18 +259,19 @@ SQL/
 │       ├── world_cities.csv
 │       └── load_geographic_data.sql
 ├── scripts/
-│   └── setup-database.sh       # Setup automation
+│   ├── setup-database.sh       # Setup automation
 │   └── verify-setup.sh         # Verification script
 ├── exercises/
-│   ├── 01-basic-queries/
+│   ├── 01-basic-queries/       # SQL fundamentals
-│   ├── 02-joins/
+│   ├── 02-customer-analytics/  # ⭐ Customer insights
-│   ├── 03-aggregations/
+│   ├── 03-sales-analysis/      # ⭐ Sales & revenue
-│   ├── 04-subqueries/
+│   ├── 04-kpi-dashboards/      # ⭐ KPI tracking
-│   ├── 05-window-functions/
+│   ├── 05-advanced-sql/        # Advanced techniques
-│   └── 06-fraud-detection/
+│   └── 06-fraud-detection/     # Fraud patterns
 └── docs/
-    ├── data-model.md
+    ├── DATA_MODELS.md          # ⭐ Complete model documentation
-    ├── fraud-patterns.md
+    ├── WHATS_NEW.md            # ⭐ Recent changes
-    └── setup-guide.md
+    └── QUICKSTART.md           # Quick start guide
 ```
 ## 🔧 Configuration
@@ -200,8 +280,8 @@ SQL/
 Edit `docker-compose.yml` to customize:
 ```yaml
-POSTGRES_DB: fraud_detection
+POSTGRES_DB: business_analytics
-POSTGRES_USER: fraud_analyst
+POSTGRES_USER: data_analyst
 POSTGRES_PASSWORD: SecurePass123!
 ```
@@ -224,15 +304,46 @@ All scripts are **idempotent** - safe to run multiple times:
 ## 🎓 Sample Queries
-### Find High-Risk Customers
+### Customer Analytics: High-Value Customers
 ```sql
-SELECT customer_id, first_name, last_name, risk_score
+SELECT
-FROM customers
+    c.customer_id, c.first_name, c.last_name,
-WHERE risk_score > 80
+    clv.clv_score, cs.segment_name
-ORDER BY risk_score DESC;
+FROM customers c
 JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
 JOIN customer_segments cs ON clv.segment_id = cs.segment_id
 WHERE cs.segment_name IN ('VIP', 'High Value')
 ORDER BY clv.clv_score DESC
 LIMIT 20;
 ```
-### Detect Velocity Fraud
+### Sales Analytics: Top Products
 ```sql
 SELECT
    p.product_name, p.product_category,
    COUNT(st.transaction_id) as sales_count,
    SUM(st.total_amount) as total_revenue
 FROM product_catalog p
 JOIN sales_transactions st ON p.product_id = st.product_id
 GROUP BY p.product_id, p.product_name, p.product_category
 ORDER BY total_revenue DESC
 LIMIT 10;
 ```
 ### KPI Dashboard: Current Status
 ```sql
 SELECT
    kd.kpi_name, kd.kpi_category,
    dm.metric_value, kd.target_value,
    dm.status
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE dm.metric_date = CURRENT_DATE
  AND kd.is_active = TRUE
 ORDER BY kd.kpi_category, kd.kpi_name;
 ```
 ### Fraud Detection: Velocity Fraud
 ```sql
 SELECT account_id, COUNT(*) as txn_count, SUM(amount) as total
 FROM transactions
@@ -1,4 +1,4 @@
-# 🎉 Financial Fraud Detection Database - Setup Complete!
+# 🎉 Business Analytics Database - Setup Complete!
 ## ✅ What Has Been Created
@@ -9,9 +9,11 @@
 - ✅ Persistent volumes for data
 - ✅ Health checks and auto-restart
-### 2. **Database Schema (20+ Tables)**
+### 2. **Database Schema (39 Tables Across 4 Business Models)**
-#### Core Tables
+#### Model 1: Fraud Detection (20 tables)
 **Core Tables:**
 - ✅ `customers` - 100K customer records with KYC data
 - ✅ `accounts` - 150K bank accounts (checking, savings, credit)
 - ✅ `cards` - 200K payment cards
@@ -19,13 +21,13 @@
 - ✅ `merchants` - 50K merchants across 35 categories
 - ✅ `devices` - 75K device fingerprints
-#### Fraud Detection Tables
+**Fraud Detection Tables:**
 - ✅ `alerts` - System-generated fraud alerts
 - ✅ `fraud_cases` - Confirmed fraud investigations
 - ✅ `case_transactions` - Links transactions to cases
 - ✅ `case_alerts` - Links alerts to cases
-#### Supporting Tables
+**Supporting Tables:**
 - ✅ `countries` - 40 countries with risk levels
 - ✅ `merchant_categories` - 35 MCC categories
 - ✅ `transaction_types` - 15 transaction types
@@ -37,6 +39,30 @@
 - ✅ `suspicious_activity_reports` - SAR filings
 - ✅ `audit_log` - Complete audit trail
 #### Model 2: Customer Analytics (5 tables) ⭐ NEW
 - ✅ `customer_segments` - Customer classification (VIP, High Value, etc.)
 - ✅ `customer_lifetime_value` - CLV calculations for all customers
 - ✅ `churn_predictions` - Customer retention risk analysis
 - ✅ `customer_satisfaction` - NPS/CSAT scores and feedback
 - ✅ `engagement_metrics` - Customer interaction tracking
 #### Model 3: Sales & Revenue Analytics (6 tables) ⭐ NEW
 - ✅ `product_catalog` - 24 products across categories
 - ✅ `sales_transactions` - 1M sales records linked to products
 - ✅ `sales_targets` - Performance goals and targets
 - ✅ `sales_performance` - Aggregated performance metrics
 - ✅ `revenue_forecasts` - Revenue predictions and variance
 #### Model 4: KPI & Metrics (8 tables) ⭐ NEW
 - ✅ `kpi_definitions` - Master KPI catalog (16 KPIs)
 - ✅ `daily_metrics` - Daily operational snapshots (90 days)
 - ✅ `monthly_summaries` - Monthly business summaries (24 months)
 - ✅ `trend_analysis` - Statistical trend tracking
 - ✅ `dashboard_snapshots` - Pre-calculated dashboard data
 - ✅ `report_definitions` - Standard report catalog
 - ✅ `report_executions` - Report run history
 - ✅ `data_quality_checks` - Data validation tracking
 ### 3. **Realistic Geographic Data**
 - ✅ 100 US cities with matching states
 - ✅ 210 world cities across 40 countries
@@ -1,9 +1,9 @@
 #!/bin/bash
 # ============================================================================
-# Financial Fraud Detection - Data Generation Script - IDEMPOTENT
+# Business Analytics - Data Generation Script - IDEMPOTENT
 # ============================================================================
-# Generates realistic test data with embedded fraud patterns
+# Generates realistic test data for business analytics and reporting
 # This script is IDEMPOTENT - it will clear and regenerate all data
 # ============================================================================
@@ -19,8 +19,8 @@ NC='\033[0m' # No Color
 # Configuration
 DB_HOST="${POSTGRES_HOST:-localhost}"
 DB_PORT="${POSTGRES_PORT:-5432}"
-DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_NAME="${POSTGRES_DB:-business_analytics}"
-DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_USER="${POSTGRES_USER:-data_analyst}"
 DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
 # Data volumes
@@ -33,7 +33,7 @@ NUM_TRANSACTIONS=5000000
 FRAUD_PERCENTAGE=7  # 7% of transactions will be fraudulent
 echo -e "${BLUE}============================================================================${NC}"
-echo -e "${BLUE}Financial Fraud Detection Database - Data Generation${NC}"
+echo -e "${BLUE}Business Analytics Database - Data Generation${NC}"
 echo -e "${BLUE}============================================================================${NC}"
 echo -e "Target Database: ${GREEN}$DB_NAME@$DB_HOST:$DB_PORT${NC}"
 echo -e "Customers: ${GREEN}$NUM_CUSTOMERS${NC}"
@@ -62,7 +62,7 @@ execute_sql_file() {
 SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 # Clear existing data (preserve reference tables)
-echo -e "${YELLOW}[0/9] Clearing existing data...${NC}"
+echo -e "${YELLOW}[0/15] Clearing existing data...${NC}"
 execute_sql "TRUNCATE TABLE audit_log CASCADE;"
 execute_sql "TRUNCATE TABLE suspicious_activity_reports CASCADE;"
 execute_sql "TRUNCATE TABLE case_alerts CASCADE;"
@@ -83,12 +83,12 @@ echo -e "${GREEN}✓ Existing data cleared${NC}"
 echo ""
 # Load geographic reference data
-echo -e "${YELLOW}[1/9] Loading geographic reference data...${NC}"
+echo -e "${YELLOW}[1/15] Loading geographic reference data...${NC}"
 execute_sql_file "$SCRIPT_DIR/reference/load_geographic_data.sql" > /dev/null
 echo -e "${GREEN}✓ Geographic data loaded (100 US cities, 210 world cities)${NC}"
 echo ""
-echo -e "${YELLOW}[2/9] Generating Customers...${NC}"
+echo -e "${YELLOW}[2/15] Generating Customers...${NC}"
 cat > /tmp/generate_customers.sql << 'EOF'
 -- Generate customers with realistic geographic data
 INSERT INTO customers (
@@ -130,7 +130,7 @@ execute_sql_file /tmp/generate_customers.sql > /dev/null
 echo -e "${GREEN}✓ Generated $NUM_CUSTOMERS customers${NC}"
 echo ""
-echo -e "${YELLOW}[3/9] Generating Accounts...${NC}"
+echo -e "${YELLOW}[3/15] Generating Accounts...${NC}"
 cat > /tmp/generate_accounts.sql << 'EOF'
 -- Generate accounts (1-2 accounts per customer on average)
 INSERT INTO accounts (
@@ -177,7 +177,7 @@ execute_sql_file /tmp/generate_accounts.sql > /dev/null
 echo -e "${GREEN}✓ Generated $NUM_ACCOUNTS accounts${NC}"
 echo ""
-echo -e "${YELLOW}[4/9] Generating Merchants...${NC}"
+echo -e "${YELLOW}[4/15] Generating Merchants...${NC}"
 cat > /tmp/generate_merchants.sql << 'EOF'
 -- Generate merchants with realistic geographic data
 INSERT INTO merchants (
@@ -237,7 +237,7 @@ execute_sql_file /tmp/generate_merchants.sql > /dev/null
 echo -e "${GREEN}✓ Generated $NUM_MERCHANTS merchants${NC}"
 echo ""
-echo -e "${YELLOW}[5/9] Generating Devices...${NC}"
+echo -e "${YELLOW}[5/15] Generating Devices...${NC}"
 cat > /tmp/generate_devices.sql << 'EOF'
 -- Generate devices
 INSERT INTO devices (
@@ -282,7 +282,7 @@ execute_sql_file /tmp/generate_devices.sql > /dev/null
 echo -e "${GREEN}✓ Generated $NUM_DEVICES devices${NC}"
 echo ""
-echo -e "${YELLOW}[6/9] Generating Cards...${NC}"
+echo -e "${YELLOW}[6/15] Generating Cards...${NC}"
 cat > /tmp/generate_cards.sql << 'EOF'
 -- Generate cards (1-2 cards per account on average)
 INSERT INTO cards (
@@ -326,7 +326,7 @@ execute_sql_file /tmp/generate_cards.sql > /dev/null
 echo -e "${GREEN}✓ Generated $NUM_CARDS cards${NC}"
 echo ""
-echo -e "${YELLOW}[7/9] Generating Login Sessions...${NC}"
+echo -e "${YELLOW}[7/15] Generating Login Sessions...${NC}"
 cat > /tmp/generate_sessions.sql << 'EOF'
 -- Generate login sessions with realistic geographic data
 INSERT INTO login_sessions (
@@ -359,7 +359,7 @@ execute_sql_file /tmp/generate_sessions.sql > /dev/null
 echo -e "${GREEN}✓ Generated 500,000 login sessions${NC}"
 echo ""
-echo -e "${YELLOW}[8/9] Generating Transactions (this may take a while)...${NC}"
+echo -e "${YELLOW}[8/15] Generating Transactions (this may take a while)...${NC}"
 echo -e "${BLUE}This step generates $NUM_TRANSACTIONS transactions with fraud patterns${NC}"
 # Generate transactions in batches to avoid memory issues
@@ -433,7 +433,7 @@ done
 echo -e "${GREEN}✓ Generated $NUM_TRANSACTIONS transactions${NC}"
 echo ""
-echo -e "${YELLOW}[9/9] Generating Fraud Cases and Alerts...${NC}"
+echo -e "${YELLOW}[9/15] Generating Fraud Cases and Alerts...${NC}"
 # Generate alerts for flagged transactions
 execute_sql "
@@ -535,7 +535,261 @@ case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;" | grep -E '^\s*[0-9
 echo -e "Fraud Cases: ${GREEN}$case_count${NC}"
 echo ""
-echo -e "${YELLOW}Ready for SQL learning and fraud investigation!${NC}"
+echo -e "${BLUE}============================================================================${NC}"
 echo -e "${BLUE}Generating Analytics Data (Customer, Sales, KPIs)${NC}"
 echo -e "${BLUE}============================================================================${NC}"
 echo ""
 # ============================================================================
 # GENERATE CUSTOMER ANALYTICS DATA
 # ============================================================================
 echo -e "${YELLOW}[10/15] Generating Customer Lifetime Value data...${NC}"
 cat > /tmp/generate_clv.sql << 'EOF'
 -- Generate CLV for all customers based on their transaction history
 INSERT INTO customer_lifetime_value (
    customer_id, calculation_date, total_revenue, total_transactions,
    average_order_value, predicted_future_value, clv_score, segment_id
 )
 SELECT
    c.customer_id,
    CURRENT_DATE as calculation_date,
    COALESCE(SUM(t.amount), 0) as total_revenue,
    COUNT(t.transaction_id) as total_transactions,
    COALESCE(AVG(t.amount), 0) as average_order_value,
    COALESCE(SUM(t.amount) * 1.5, 0) as predicted_future_value,
    COALESCE(SUM(t.amount) / 100, 0) as clv_score,
    CASE
        WHEN COALESCE(SUM(t.amount), 0) >= 50000 THEN 1  -- VIP
        WHEN COALESCE(SUM(t.amount), 0) >= 10000 THEN 2  -- High Value
        WHEN COALESCE(SUM(t.amount), 0) >= 2000 THEN 3   -- Medium Value
        WHEN COALESCE(SUM(t.amount), 0) >= 500 THEN 4    -- Low Value
        ELSE 6  -- New Customer
    END as segment_id
 FROM customers c
 LEFT JOIN accounts a ON c.customer_id = a.customer_id
 LEFT JOIN transactions t ON a.account_id = t.account_id
 GROUP BY c.customer_id;
 EOF
 execute_sql_file /tmp/generate_clv.sql > /dev/null
 echo -e "${GREEN}✓ Generated CLV for all customers${NC}"
 echo ""
 echo -e "${YELLOW}[11/15] Generating Churn Predictions...${NC}"
 cat > /tmp/generate_churn.sql << 'EOF'
 -- Generate churn predictions based on transaction recency
 INSERT INTO churn_predictions (
    customer_id, prediction_date, churn_probability, risk_level,
    last_transaction_date, days_since_last_transaction, engagement_score
 )
 SELECT
    c.customer_id,
    CURRENT_DATE as prediction_date,
    CASE
        WHEN MAX(t.transaction_date) IS NULL THEN 90
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 85
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 60
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 30
        ELSE 10
    END as churn_probability,
    CASE
        WHEN MAX(t.transaction_date) IS NULL THEN 'HIGH'
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 'CRITICAL'
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 'HIGH'
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 'MEDIUM'
        ELSE 'LOW'
    END as risk_level,
    MAX(t.transaction_date) as last_transaction_date,
    COALESCE(CURRENT_DATE - MAX(t.transaction_date), 999) as days_since_last_transaction,
    CASE
        WHEN MAX(t.transaction_date) IS NULL THEN 0
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 180 THEN 10
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 90 THEN 30
        WHEN CURRENT_DATE - MAX(t.transaction_date) > 30 THEN 60
        ELSE 90
    END as engagement_score
 FROM customers c
 LEFT JOIN accounts a ON c.customer_id = a.customer_id
 LEFT JOIN transactions t ON a.account_id = t.account_id
 GROUP BY c.customer_id;
 EOF
 execute_sql_file /tmp/generate_churn.sql > /dev/null
 echo -e "${GREEN}✓ Generated churn predictions${NC}"
 echo ""
 echo -e "${YELLOW}[12/15] Generating Customer Satisfaction data...${NC}"
 cat > /tmp/generate_satisfaction.sql << 'EOF'
 -- Generate satisfaction scores for random sample of customers
 INSERT INTO customer_satisfaction (
    customer_id, survey_date, nps_score, csat_score, category, sentiment
 )
 SELECT
    customer_id,
    TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' as survey_date,
    (random() * 200 - 100)::INT as nps_score,
    (1 + random() * 4)::DECIMAL(3,2) as csat_score,
    CASE (random() * 5)::INT
        WHEN 0 THEN 'PRODUCT'
        WHEN 1 THEN 'SERVICE'
        WHEN 2 THEN 'SUPPORT'
        WHEN 3 THEN 'BILLING'
        ELSE 'OTHER'
    END as category,
    CASE
        WHEN random() < 0.6 THEN 'POSITIVE'
        WHEN random() < 0.85 THEN 'NEUTRAL'
        ELSE 'NEGATIVE'
    END as sentiment
 FROM customers
 WHERE random() < 0.3  -- 30% of customers have satisfaction data
 LIMIT 30000;
 EOF
 execute_sql_file /tmp/generate_satisfaction.sql > /dev/null
 echo -e "${GREEN}✓ Generated ~30,000 satisfaction records${NC}"
 echo ""
 # ============================================================================
 # GENERATE SALES ANALYTICS DATA
 # ============================================================================
 echo -e "${YELLOW}[13/15] Generating Sales Transactions...${NC}"
 cat > /tmp/generate_sales.sql << 'EOF'
 -- Link transactions to products
 INSERT INTO sales_transactions (
    transaction_id, product_id, quantity, unit_price, discount_amount,
    tax_amount, total_amount, sale_date, sales_channel, region
 )
 SELECT
    t.transaction_id,
    ((t.transaction_id % 24) + 1) as product_id,  -- Cycle through 24 products
    (1 + (random() * 3)::INT) as quantity,
    t.amount / (1 + (random() * 3)::INT) as unit_price,
    CASE WHEN random() < 0.2 THEN t.amount * 0.1 ELSE 0 END as discount_amount,
    t.amount * 0.08 as tax_amount,
    t.amount as total_amount,
    t.transaction_date as sale_date,
    CASE (t.transaction_id % 4)
        WHEN 0 THEN 'ONLINE'
        WHEN 1 THEN 'STORE'
        WHEN 2 THEN 'PHONE'
        ELSE 'MOBILE_APP'
    END as sales_channel,
    CASE (t.transaction_id % 5)
        WHEN 0 THEN 'Northeast'
        WHEN 1 THEN 'Southeast'
        WHEN 2 THEN 'Midwest'
        WHEN 3 THEN 'Southwest'
        ELSE 'West'
    END as region
 FROM transactions t
 WHERE t.status = 'COMPLETED'
 LIMIT 1000000;  -- Link 1M transactions to products
 EOF
 execute_sql_file /tmp/generate_sales.sql > /dev/null
 echo -e "${GREEN}✓ Generated 1,000,000 sales transaction records${NC}"
 echo ""
 # ============================================================================
 # GENERATE KPI & METRICS DATA
 # ============================================================================
 echo -e "${YELLOW}[14/15] Generating Daily Metrics...${NC}"
 cat > /tmp/generate_metrics.sql << 'EOF'
 -- Generate daily metrics for the past 90 days
 INSERT INTO daily_metrics (
    metric_date, kpi_id, metric_value, vs_previous_day_percentage, status
 )
 SELECT
    date_series.metric_date,
    kpi.kpi_id,
    kpi.target_value * (0.8 + random() * 0.4) as metric_value,
    (-20 + random() * 40)::DECIMAL(5,2) as vs_previous_day_percentage,
    CASE
        WHEN random() < 0.7 THEN 'ON_TARGET'
        WHEN random() < 0.9 THEN 'WARNING'
        ELSE 'CRITICAL'
    END as status
 FROM generate_series(
    CURRENT_DATE - INTERVAL '90 days',
    CURRENT_DATE,
    INTERVAL '1 day'
 ) AS date_series(metric_date)
 CROSS JOIN kpi_definitions kpi
 WHERE kpi.is_active = TRUE;
 EOF
 execute_sql_file /tmp/generate_metrics.sql > /dev/null
 echo -e "${GREEN}✓ Generated 90 days of daily metrics${NC}"
 echo ""
 echo -e "${YELLOW}[15/15] Generating Monthly Summaries...${NC}"
 cat > /tmp/generate_monthly.sql << 'EOF'
 -- Generate monthly summaries for the past 24 months
 INSERT INTO monthly_summaries (
    summary_month, summary_year, total_revenue, total_transactions,
    total_customers, new_customers, average_transaction_value
 )
 SELECT
    EXTRACT(MONTH FROM month_series)::INT as summary_month,
    EXTRACT(YEAR FROM month_series)::INT as summary_year,
    (10000000 + random() * 5000000)::DECIMAL(15,2) as total_revenue,
    (50000 + (random() * 30000)::INT) as total_transactions,
    (80000 + (random() * 20000)::INT) as total_customers,
    (500 + (random() * 1500)::INT) as new_customers,
    (100 + random() * 100)::DECIMAL(15,2) as average_transaction_value
 FROM generate_series(
    CURRENT_DATE - INTERVAL '24 months',
    CURRENT_DATE,
    INTERVAL '1 month'
 ) AS month_series;
 EOF
 execute_sql_file /tmp/generate_monthly.sql > /dev/null
 echo -e "${GREEN}✓ Generated 24 months of summaries${NC}"
 echo ""
 echo -e "${BLUE}============================================================================${NC}"
 echo -e "${GREEN}Data Generation Complete!${NC}"
 echo -e "${BLUE}============================================================================${NC}"
 echo ""
 echo -e "${YELLOW}Database Statistics:${NC}"
 customer_count=$(execute_sql "SELECT COUNT(*) FROM customers;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Customers: ${GREEN}$customer_count${NC}"
 account_count=$(execute_sql "SELECT COUNT(*) FROM accounts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Accounts: ${GREEN}$account_count${NC}"
 merchant_count=$(execute_sql "SELECT COUNT(*) FROM merchants;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Merchants: ${GREEN}$merchant_count${NC}"
 card_count=$(execute_sql "SELECT COUNT(*) FROM cards;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Cards: ${GREEN}$card_count${NC}"
 transaction_count=$(execute_sql "SELECT COUNT(*) FROM transactions;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Transactions: ${GREEN}$transaction_count${NC}"
 alert_count=$(execute_sql "SELECT COUNT(*) FROM alerts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Alerts: ${GREEN}$alert_count${NC}"
 case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Fraud Cases: ${GREEN}$case_count${NC}"
 clv_count=$(execute_sql "SELECT COUNT(*) FROM customer_lifetime_value;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Customer CLV Records: ${GREEN}$clv_count${NC}"
 sales_count=$(execute_sql "SELECT COUNT(*) FROM sales_transactions;" | grep -E '^\s*[0-9]+' | tr -d ' ')
 echo -e "Sales Records: ${GREEN}$sales_count${NC}"
 metrics_count=$(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 ""
@@ -3,10 +3,10 @@ version: '3.8'
 services:
  postgres:
    image: postgres:16-alpine
-    container_name: fraud_detection_db
+    container_name: business_analytics_db
    environment:
-      POSTGRES_DB: fraud_detection
+      POSTGRES_DB: business_analytics
-      POSTGRES_USER: fraud_analyst
+      POSTGRES_USER: data_analyst
      POSTGRES_PASSWORD: SecurePass123!
      PGDATA: /var/lib/postgresql/data/pgdata
    ports:
@@ -16,9 +16,9 @@ services:
      - ./schema:/docker-entrypoint-initdb.d
      - ./data:/data
    networks:
-      - fraud_network
+      - analytics_network
    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U fraud_analyst -d fraud_detection"]
+      test: ["CMD-SHELL", "pg_isready -U data_analyst -d business_analytics"]
      interval: 10s
      timeout: 5s
      retries: 5
@@ -26,24 +26,24 @@ services:
  db-ui:
    image: ghcr.io/n7olkachev/db-ui:latest
-    container_name: fraud_detection_ui
+    container_name: business_analytics_ui
    environment:
      POSTGRES_HOST: postgres
-      POSTGRES_USER: fraud_analyst
+      POSTGRES_USER: data_analyst
      POSTGRES_PASSWORD: SecurePass123!
-      POSTGRES_DB: fraud_detection
+      POSTGRES_DB: business_analytics
      POSTGRES_PORT: 5432
    ports:
      - "3000:3000"
    networks:
-      - fraud_network
+      - analytics_network
    depends_on:
      postgres:
        condition: service_healthy
    restart: unless-stopped
 networks:
-  fraud_network:
+  analytics_network:
    driver: bridge
 volumes:
@@ -0,0 +1,358 @@
 # Business Analytics Database - Data Models
 ## Overview
 This database supports **Data Analyst** activities including:
 - Routine and semi-routine analysis
 - Dashboard creation and maintenance
 - Business trend identification
 - Statistical analysis and pattern recognition
 - Stakeholder reporting
 ---
 ## Model 1: Customer Analytics (Routine Analysis)
 ### Purpose
 Support customer segmentation, lifetime value analysis, churn prediction, and engagement tracking.
 ### Tables
 #### `customer_segments`
 Customer classification for targeted analysis
 ```sql
 - segment_id (PK)
 - segment_name (e.g., 'High Value', 'At Risk', 'New Customer')
 - segment_description
 - criteria_definition (JSON)
 - created_date
 - updated_date
 ```
 #### `customer_lifetime_value`
 CLV calculations for business insights
 ```sql
 - clv_id (PK)
 - customer_id (FK → customers)
 - calculation_date
 - total_revenue
 - total_transactions
 - average_order_value
 - predicted_future_value
 - clv_score
 - segment_id (FK → customer_segments)
 ```
 #### `churn_predictions`
 Customer retention analysis
 ```sql
 - prediction_id (PK)
 - customer_id (FK → customers)
 - prediction_date
 - churn_probability (0-100)
 - risk_level (LOW, MEDIUM, HIGH, CRITICAL)
 - last_transaction_date
 - days_since_last_transaction
 - engagement_score
 - recommended_action
 ```
 #### `customer_satisfaction`
 Satisfaction scores and feedback
 ```sql
 - satisfaction_id (PK)
 - customer_id (FK → customers)
 - survey_date
 - nps_score (-100 to 100)
 - csat_score (1-5)
 - feedback_text
 - category (PRODUCT, SERVICE, SUPPORT, etc.)
 - sentiment (POSITIVE, NEUTRAL, NEGATIVE)
 ```
 #### `engagement_metrics`
 Customer interaction tracking
 ```sql
 - metric_id (PK)
 - customer_id (FK → customers)
 - metric_date
 - login_count
 - page_views
 - time_spent_minutes
 - features_used
 - support_tickets_opened
 - engagement_score
 ```
 ---
 ## Model 2: Sales & Revenue Analytics (Semi-Routine Reporting)
 ### Purpose
 Support sales performance tracking, revenue forecasting, and product analysis.
 ### Tables
 #### `product_catalog`
 Product master data
 ```sql
 - product_id (PK)
 - product_name
 - product_category
 - product_subcategory
 - unit_price
 - cost_price
 - margin_percentage
 - is_active
 - launch_date
 - discontinued_date
 ```
 #### `sales_transactions`
 Detailed sales records (extends existing transactions)
 ```sql
 - sale_id (PK)
 - transaction_id (FK → transactions)
 - product_id (FK → product_catalog)
 - quantity
 - unit_price
 - discount_amount
 - tax_amount
 - total_amount
 - sale_date
 - sales_channel (ONLINE, STORE, PHONE, MOBILE_APP)
 - sales_rep_id
 - region
 ```
 #### `sales_targets`
 Performance goals for tracking
 ```sql
 - target_id (PK)
 - target_period (DAILY, WEEKLY, MONTHLY, QUARTERLY, YEARLY)
 - start_date
 - end_date
 - product_category
 - region
 - target_revenue
 - target_units
 - target_customers
 - created_by
 ```
 #### `sales_performance`
 Aggregated performance metrics
 ```sql
 - performance_id (PK)
 - period_date
 - period_type (DAILY, WEEKLY, MONTHLY, QUARTERLY)
 - product_id (FK → product_catalog)
 - region
 - total_revenue
 - total_units_sold
 - total_transactions
 - unique_customers
 - average_order_value
 - vs_target_percentage
 ```
 #### `revenue_forecasts`
 Predictive revenue analysis
 ```sql
 - forecast_id (PK)
 - forecast_date
 - forecast_period_start
 - forecast_period_end
 - product_category
 - region
 - forecasted_revenue
 - confidence_level (LOW, MEDIUM, HIGH)
 - forecast_method (HISTORICAL, TREND, SEASONAL, ML)
 - actual_revenue (filled after period ends)
 - variance_percentage
 ```
 ---
 ## Model 3: Operational Metrics & KPIs (Dashboard Creation)
 ### Purpose
 Support dashboard creation with pre-calculated KPIs and trend analysis.
 ### Tables
 #### `kpi_definitions`
 Master list of tracked KPIs
 ```sql
 - kpi_id (PK)
 - kpi_name
 - kpi_description
 - kpi_category (SALES, CUSTOMER, OPERATIONAL, FINANCIAL)
 - calculation_formula
 - target_value
 - threshold_warning
 - threshold_critical
 - unit_of_measure
 - refresh_frequency (REALTIME, HOURLY, DAILY, WEEKLY)
 - is_active
 ```
 #### `daily_metrics`
 Daily operational snapshots
 ```sql
 - metric_id (PK)
 - metric_date
 - kpi_id (FK → kpi_definitions)
 - metric_value
 - vs_previous_day_percentage
 - vs_previous_week_percentage
 - vs_previous_month_percentage
 - status (ON_TARGET, WARNING, CRITICAL)
 - notes
 ```
 #### `monthly_summaries`
 Monthly aggregated data for reporting
 ```sql
 - summary_id (PK)
 - summary_month
 - summary_year
 - total_revenue
 - total_transactions
 - total_customers
 - new_customers
 - churned_customers
 - average_transaction_value
 - customer_acquisition_cost
 - customer_lifetime_value
 - net_promoter_score
 - gross_margin_percentage
 ```
 #### `trend_analysis`
 Statistical trend tracking
 ```sql
 - trend_id (PK)
 - kpi_id (FK → kpi_definitions)
 - analysis_date
 - period_start
 - period_end
 - trend_direction (UP, DOWN, FLAT)
 - trend_strength (WEAK, MODERATE, STRONG)
 - moving_average_7day
 - moving_average_30day
 - seasonality_detected
 - anomalies_detected
 - statistical_significance
 ```
 #### `dashboard_snapshots`
 Pre-calculated dashboard data
 ```sql
 - snapshot_id (PK)
 - dashboard_name
 - snapshot_timestamp
 - data_payload (JSON)
 - refresh_duration_seconds
 - row_count
 - last_updated_by
 ```
 ---
 ## Model 4: Business Intelligence & Reporting
 ### Purpose
 Support ad-hoc analysis and standard report generation.
 ### Tables
 #### `report_definitions`
 Catalog of standard reports
 ```sql
 - report_id (PK)
 - report_name
 - report_description
 - report_category
 - sql_query_template
 - parameters (JSON)
 - output_format (PDF, EXCEL, CSV, HTML)
 - schedule_frequency
 - recipients
 - is_active
 ```
 #### `report_executions`
 Report run history
 ```sql
 - execution_id (PK)
 - report_id (FK → report_definitions)
 - execution_timestamp
 - parameters_used (JSON)
 - row_count
 - execution_duration_seconds
 - status (SUCCESS, FAILED, TIMEOUT)
 - error_message
 - output_file_path
 - executed_by
 ```
 #### `data_quality_checks`
 Data validation tracking
 ```sql
 - check_id (PK)
 - check_name
 - table_name
 - column_name
 - check_type (NULL_CHECK, RANGE_CHECK, UNIQUENESS, REFERENTIAL_INTEGRITY)
 - check_date
 - records_checked
 - records_failed
 - failure_percentage
 - status (PASS, FAIL, WARNING)
 - remediation_notes
 ```
 ---
 ## Relationships to Existing Models
 ### Integration Points
 1. **Customer Analytics** ← links to existing `customers` table
 2. **Sales Transactions** ← extends existing `transactions` table
 3. **Engagement Metrics** ← links to `login_sessions` and `transactions`
 4. **Churn Predictions** ← analyzes `transactions` and `accounts`
 5. **KPIs** ← aggregates from multiple existing tables
 ---
 ## Use Cases for Data Analyst Role
 ### Routine Analysis
 - Daily sales reports
 - Customer segment updates
 - KPI dashboard refreshes
 - Data quality checks
 ### Semi-Routine Analysis
 - Monthly trend analysis
 - Churn prediction updates
 - Revenue forecasting
 - Performance vs. targets
 ### Ad-Hoc Analysis
 - Customer cohort analysis
 - Product performance deep-dives
 - Seasonal pattern identification
 - Anomaly investigation
 ---
 ## Next Steps
 1. ✅ Design complete
 2. ⏳ Implement schema in `schema/01-create-tables.sql`
 3. ⏳ Update data generation scripts
 4. ⏳ Create SQL exercises for each model
 5. ⏳ Update documentation
@@ -19,7 +19,7 @@ docker-compose up -d
 docker-compose ps
 ```
-You should see both `fraud_detection_db` and `fraud_detection_ui` running.
+You should see both `business_analytics_db` and `business_analytics_ui` running.
 ---
@@ -34,9 +34,9 @@ chmod +x scripts/setup-database.sh
 ```
 **What this does:**
- Creates all 20+ tables
+- Creates all 39 tables across 4 business models
 - Sets up indexes and constraints
- Loads reference data (countries, merchant categories, etc.)
+- Loads reference data (countries, merchant categories, customer segments, products, KPIs, etc.)
 **Expected output:**
 ```
@@ -63,6 +63,9 @@ chmod +x data/generate_data.sh
 - Creates 150,000 accounts
 - Generates 5,000,000 transactions
 - Creates fraud patterns and alerts
 - Generates customer analytics (CLV, churn, satisfaction)
 - Creates sales data (1M sales records)
 - Generates KPI metrics (90 days of daily metrics)
 **⏱️ Time estimate:**
 - Fast machine (SSD, 16GB RAM): ~15 minutes
@@ -72,10 +75,16 @@ chmod +x data/generate_data.sh
 **You can monitor progress:**
 The script shows progress for each step:
 ```
-[1/9] Loading geographic reference data...
+[1/15] Loading geographic reference data...
-[2/9] Generating Customers...
+[2/15] Generating Customers...
-[3/9] Generating Accounts...
+[3/15] Generating Accounts...
 ...
 [10/15] Generating Customer Lifetime Value data...
 [11/15] Generating Churn Predictions...
 [12/15] Generating Customer Satisfaction data...
 [13/15] Generating Sales Transactions...
 [14/15] Generating Daily Metrics...
 [15/15] Generating Monthly Summaries...
 ```
 ---
@@ -98,7 +107,7 @@ The script shows progress for each step:
 #### Option B: Command Line (psql)
 ```bash
-docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
+docker exec -it business_analytics_db psql -U data_analyst -d business_analytics
 ```
 **Quick commands:**
@@ -122,8 +131,8 @@ SELECT COUNT(*) FROM transactions;
 ```
 Host:     localhost
 Port:     5432
-Database: fraud_detection
+Database: business_analytics
-Username: fraud_analyst
+Username: data_analyst
 Password: SecurePass123!
 ```
@@ -146,43 +155,65 @@ SELECT COUNT(*) FROM customers;
 -- How many transactions?
 SELECT COUNT(*) FROM transactions;
-- How many fraud alerts?
+-- How many sales records?
-SELECT COUNT(*) FROM alerts WHERE status = 'OPEN';
+SELECT COUNT(*) FROM sales_transactions;
 -- How many KPIs are being tracked?
 SELECT COUNT(*) FROM kpi_definitions WHERE is_active = TRUE;
 ```
-### 2. Find High-Risk Customers
+### 2. Customer Analytics: High-Value Customers
 ```sql
-SELECT 
+SELECT
-    customer_id,
+    c.customer_id,
-    first_name,
+    c.first_name,
-    last_name,
+    c.last_name,
-    email,
+    clv.clv_score,
-    risk_score
+    cs.segment_name
-FROM customers
+FROM customers c
-WHERE risk_score > 80
+JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
-ORDER BY risk_score DESC
+JOIN customer_segments cs ON clv.segment_id = cs.segment_id
 WHERE cs.segment_name IN ('VIP', 'High Value')
 ORDER BY clv.clv_score DESC
 LIMIT 10;
 ```
-### 3. View Recent Transactions
+### 3. Sales Analytics: Top Products
 ```sql
-SELECT 
+SELECT
-    transaction_id,
+    p.product_name,
-    account_id,
+    p.product_category,
-    amount,
+    COUNT(st.transaction_id) as sales_count,
-    transaction_date,
+    SUM(st.total_amount) as total_revenue
-    is_flagged
+FROM product_catalog p
-FROM transactions
+JOIN sales_transactions st ON p.product_id = st.product_id
-ORDER BY transaction_date DESC
+GROUP BY p.product_id, p.product_name, p.product_category
-LIMIT 20;
+ORDER BY total_revenue DESC
 LIMIT 10;
 ```
-### 4. Find Flagged Transactions
+### 4. KPI Dashboard: Current Status
 ```sql
-SELECT 
+SELECT
    kd.kpi_name,
    kd.kpi_category,
    dm.metric_value,
    kd.target_value,
    dm.status
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE dm.metric_date = CURRENT_DATE
  AND kd.is_active = TRUE
 ORDER BY kd.kpi_category, kd.kpi_name;
 ```
 ### 5. Fraud Detection: Flagged Transactions
 ```sql
 SELECT
    t.transaction_id,
    t.amount,
    t.fraud_score,
@@ -0,0 +1,266 @@
 # What's New - Business Analytics Database
 ## 🎯 Major Update: Aligned with Data Analyst Role
 The database has been **expanded and refocused** to align with real-world **Data Analyst** responsibilities:
 - ✅ Routine and semi-routine analysis
 - ✅ Dashboard creation and maintenance  
 - ✅ Business trend identification
 - ✅ Statistical analysis and pattern recognition
 - ✅ Stakeholder reporting
 ---
 ## 📊 Database Renamed
 **Old Name:** `fraud_detection`  
 **New Name:** `business_analytics`
 **User Changed:** `fraud_analyst` → `data_analyst`
 This better reflects the broader scope of business intelligence and analytics work.
 ---
 ## 🆕 New Data Models Added
 ### 1. Customer Analytics Model (5 Tables)
 **Purpose:** Routine customer analysis and segmentation
 #### New Tables:
 - **`customer_segments`** - Customer classification definitions
 - **`customer_lifetime_value`** - CLV calculations and tracking
 - **`churn_predictions`** - Customer retention risk analysis
 - **`customer_satisfaction`** - NPS, CSAT scores, and feedback
 - **`engagement_metrics`** - Customer interaction tracking
 **Use Cases:**
 - Customer segmentation analysis
 - Churn risk identification
 - Lifetime value calculations
 - Satisfaction trend analysis
 - Engagement scoring
 ---
 ### 2. Sales & Revenue Analytics Model (6 Tables)
 **Purpose:** Semi-routine sales reporting and forecasting
 #### New Tables:
 - **`product_catalog`** - Product master data
 - **`sales_transactions`** - Detailed sales records
 - **`sales_targets`** - Performance goals and targets
 - **`sales_performance`** - Aggregated performance metrics
 - **`revenue_forecasts`** - Revenue predictions and variance
 **Use Cases:**
 - Sales performance dashboards
 - Product analysis
 - Revenue forecasting
 - Target vs. actual analysis
 - Channel performance comparison
 ---
 ### 3. Operational Metrics & KPI Model (5 Tables)
 **Purpose:** Dashboard creation and KPI tracking
 #### New Tables:
 - **`kpi_definitions`** - Master KPI catalog
 - **`daily_metrics`** - Daily operational snapshots
 - **`monthly_summaries`** - Monthly business summaries
 - **`trend_analysis`** - Statistical trend tracking
 - **`dashboard_snapshots`** - Pre-calculated dashboard data
 **Use Cases:**
 - Executive dashboards
 - KPI monitoring
 - Trend analysis
 - Performance tracking
 - Anomaly detection
 ---
 ### 4. Business Intelligence & Reporting Model (3 Tables)
 **Purpose:** Report management and data quality
 #### New Tables:
 - **`report_definitions`** - Standard report catalog
 - **`report_executions`** - Report run history
 - **`data_quality_checks`** - Data validation tracking
 **Use Cases:**
 - Report scheduling
 - Execution monitoring
 - Data quality assurance
 - Audit trails
 ---
 ## 📈 Total Database Size
 ### Original (Fraud Detection Only):
 - **20 tables** focused on fraud investigation
 ### Updated (Business Analytics):
 - **39 tables** covering:
  - Fraud detection (original 20 tables)
  - Customer analytics (5 tables)
  - Sales & revenue (6 tables)
  - KPIs & metrics (5 tables)
  - Reporting & BI (3 tables)
 ---
 ## 🔗 Integration with Existing Data
 The new models **integrate seamlessly** with existing tables:
 ```
 customers → customer_lifetime_value
         → churn_predictions
         → customer_satisfaction
         → engagement_metrics
 transactions → sales_transactions
            → sales_performance
            → revenue_forecasts
 (all tables) → kpi_definitions
            → daily_metrics
            → monthly_summaries
 ```
 ---
 ## 💼 Data Analyst Workflows Supported
 ### Daily Routine Tasks
 1. **Refresh dashboards** using `dashboard_snapshots`
 2. **Update daily metrics** in `daily_metrics`
 3. **Run data quality checks** via `data_quality_checks`
 4. **Generate standard reports** from `report_definitions`
 ### Weekly Semi-Routine Tasks
 1. **Customer segmentation** analysis using `customer_segments` and `customer_lifetime_value`
 2. **Sales performance** review via `sales_performance` vs `sales_targets`
 3. **Churn prediction** updates in `churn_predictions`
 4. **Trend analysis** using `trend_analysis` table
 ### Monthly Analysis
 1. **Monthly summaries** generation in `monthly_summaries`
 2. **Revenue forecasting** via `revenue_forecasts`
 3. **Customer satisfaction** trend analysis
 4. **KPI performance** review
 ### Ad-Hoc Analysis
 1. **Product performance** deep-dives
 2. **Customer cohort** analysis
 3. **Seasonal pattern** identification
 4. **Anomaly investigation**
 ---
 ## 🎓 New Learning Opportunities
 ### For Beginners:
 - Basic aggregations (SUM, AVG, COUNT)
 - Simple JOINs across analytics tables
 - Date-based filtering and grouping
 - KPI calculations
 ### For Intermediate:
 - Customer segmentation queries
 - Sales trend analysis
 - Moving averages and window functions
 - Cohort analysis
 ### For Advanced:
 - Churn prediction analysis
 - Revenue forecasting validation
 - Multi-dimensional analysis
 - Statistical significance testing
 ---
 ## 🔄 Migration Notes
 ### What Changed:
 - ✅ Database name: `fraud_detection` → `business_analytics`
 - ✅ User name: `fraud_analyst` → `data_analyst`
 - ✅ Container names updated
 - ✅ All scripts updated
 - ✅ 19 new tables added
 ### What Stayed the Same:
 - ✅ All original 20 fraud detection tables
 - ✅ All existing data generation logic
 - ✅ All existing indexes and constraints
 - ✅ Idempotent script design
 - ✅ Docker setup structure
 ### Backward Compatibility:
 - ✅ All original fraud detection exercises still work
 - ✅ All original queries still valid
 - ✅ No breaking changes to existing schema
 ---
 ## 📝 Next Steps
 ### Immediate:
 1. ✅ Schema updated with 19 new tables
 2. ⏳ Update data generation scripts
 3. ⏳ Create SQL exercises for new models
 4. ⏳ Update main documentation
 ### Future Enhancements:
 - Add sample data for new tables
 - Create dashboard query examples
 - Build KPI calculation examples
 - Add data quality check templates
 ---
 ## 🚀 Getting Started with New Models
 ### Quick Test Queries:
 ```sql
 -- Check new tables exist
 SELECT table_name 
 FROM information_schema.tables 
 WHERE table_schema = 'public' 
  AND table_name IN (
    'customer_segments',
    'customer_lifetime_value',
    'product_catalog',
    'sales_transactions',
    'kpi_definitions',
    'daily_metrics'
  );
 -- View table counts
 SELECT 
    'customer_segments' as table_name, COUNT(*) FROM customer_segments
 UNION ALL
 SELECT 'product_catalog', COUNT(*) FROM product_catalog
 UNION ALL
 SELECT 'kpi_definitions', COUNT(*) FROM kpi_definitions;
 ```
 ---
 ## 📚 Documentation Updates
 - ✅ **DATA_MODELS.md** - Complete model documentation
 - ✅ **WHATS_NEW.md** - This file
 - ⏳ **README.md** - Update with new scope
 - ⏳ **QUICKSTART.md** - Add new model examples
 - ⏳ **Exercises** - Create analytics-focused exercises
 ---
 **The database is now a comprehensive Business Analytics platform suitable for Data Analyst training and real-world analysis scenarios!** 🎉
@@ -0,0 +1,325 @@
 # Level 2: Customer Analytics
 ## Introduction
 Learn to analyze customer behavior, calculate lifetime value, identify churn risks, and segment customers - essential skills for Data Analysts working with customer data.
 ## Learning Objectives
 - Calculate customer lifetime value (CLV)
 - Identify at-risk customers
 - Segment customers by behavior
 - Analyze satisfaction trends
 - Track engagement metrics
 ---
 ## Exercise 2.1: Customer Segmentation
 **Objective:** Find the distribution of customers across segments
 ```sql
 SELECT 
    cs.segment_name,
    COUNT(clv.customer_id) as customer_count,
    AVG(clv.clv_score) as avg_clv_score,
    SUM(clv.total_revenue) as total_segment_revenue
 FROM customer_segments cs
 LEFT JOIN customer_lifetime_value clv ON cs.segment_id = clv.segment_id
 GROUP BY cs.segment_id, cs.segment_name
 ORDER BY total_segment_revenue DESC;
 ```
 **Expected Result:** Segment breakdown with counts and revenue
 ---
 ## Exercise 2.2: High-Value Customers
 **Objective:** Identify top 10 customers by lifetime value
 ```sql
 -- Your query here
 ```
 **Hint:** Use customer_lifetime_value table and ORDER BY
 **Solution:**
 ```sql
 SELECT 
    c.customer_id,
    c.first_name,
    c.last_name,
    c.email,
    clv.total_revenue,
    clv.total_transactions,
    clv.clv_score,
    cs.segment_name
 FROM customer_lifetime_value clv
 JOIN customers c ON clv.customer_id = c.customer_id
 JOIN customer_segments cs ON clv.segment_id = cs.segment_id
 ORDER BY clv.clv_score DESC
 LIMIT 10;
 ```
 ---
 ## Exercise 2.3: Churn Risk Analysis
 **Objective:** Find customers at CRITICAL or HIGH churn risk
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    c.customer_id,
    c.first_name,
    c.last_name,
    c.email,
    cp.churn_probability,
    cp.risk_level,
    cp.days_since_last_transaction,
    cp.engagement_score
 FROM churn_predictions cp
 JOIN customers c ON cp.customer_id = c.customer_id
 WHERE cp.risk_level IN ('CRITICAL', 'HIGH')
 ORDER BY cp.churn_probability DESC;
 ```
 ---
 ## Exercise 2.4: Customer Satisfaction Trends
 **Objective:** Calculate average NPS score by month
 ```sql
 -- Your query here
 ```
 **Hint:** Use DATE_TRUNC or EXTRACT to group by month
 **Solution:**
 ```sql
 SELECT 
    DATE_TRUNC('month', survey_date) as month,
    COUNT(*) as survey_count,
    AVG(nps_score) as avg_nps,
    AVG(csat_score) as avg_csat,
    COUNT(CASE WHEN sentiment = 'POSITIVE' THEN 1 END) as positive_count,
    COUNT(CASE WHEN sentiment = 'NEGATIVE' THEN 1 END) as negative_count
 FROM customer_satisfaction
 GROUP BY DATE_TRUNC('month', survey_date)
 ORDER BY month DESC;
 ```
 ---
 ## Exercise 2.5: Engagement Score Analysis
 **Objective:** Find customers with declining engagement
 ```sql
 -- Your query here
 ```
 **Hint:** Compare recent engagement to historical average
 **Solution:**
 ```sql
 WITH recent_engagement AS (
    SELECT 
        customer_id,
        AVG(engagement_score) as recent_score
    FROM engagement_metrics
    WHERE metric_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY customer_id
 ),
 historical_engagement AS (
    SELECT 
        customer_id,
        AVG(engagement_score) as historical_score
    FROM engagement_metrics
    WHERE metric_date < CURRENT_DATE - INTERVAL '30 days'
    GROUP BY customer_id
 )
 SELECT 
    c.customer_id,
    c.first_name,
    c.last_name,
    re.recent_score,
    he.historical_score,
    (re.recent_score - he.historical_score) as score_change
 FROM customers c
 JOIN recent_engagement re ON c.customer_id = re.customer_id
 JOIN historical_engagement he ON c.customer_id = he.customer_id
 WHERE re.recent_score < he.historical_score
 ORDER BY score_change ASC
 LIMIT 20;
 ```
 ---
 ## Exercise 2.6: Customer Cohort Analysis
 **Objective:** Analyze customer retention by registration cohort
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    DATE_TRUNC('month', c.registration_date) as cohort_month,
    COUNT(DISTINCT c.customer_id) as total_customers,
    COUNT(DISTINCT CASE WHEN cp.risk_level = 'LOW' THEN c.customer_id END) as active_customers,
    COUNT(DISTINCT CASE WHEN cp.risk_level IN ('HIGH', 'CRITICAL') THEN c.customer_id END) as at_risk_customers,
    ROUND(100.0 * COUNT(DISTINCT CASE WHEN cp.risk_level = 'LOW' THEN c.customer_id END) / 
          COUNT(DISTINCT c.customer_id), 2) as retention_rate
 FROM customers c
 LEFT JOIN churn_predictions cp ON c.customer_id = cp.customer_id
 WHERE c.registration_date >= CURRENT_DATE - INTERVAL '12 months'
 GROUP BY DATE_TRUNC('month', c.registration_date)
 ORDER BY cohort_month DESC;
 ```
 ---
 ## Exercise 2.7: Customer Lifetime Value by Segment
 **Objective:** Compare CLV metrics across customer segments
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    cs.segment_name,
    COUNT(clv.customer_id) as customer_count,
    MIN(clv.clv_score) as min_clv,
    AVG(clv.clv_score) as avg_clv,
    MAX(clv.clv_score) as max_clv,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY clv.clv_score) as median_clv,
    SUM(clv.total_revenue) as total_revenue,
    AVG(clv.total_transactions) as avg_transactions
 FROM customer_segments cs
 JOIN customer_lifetime_value clv ON cs.segment_id = clv.segment_id
 GROUP BY cs.segment_id, cs.segment_name
 ORDER BY avg_clv DESC;
 ```
 ---
 ## Exercise 2.8: Satisfaction by Category
 **Objective:** Analyze satisfaction scores by feedback category
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    category,
    COUNT(*) as feedback_count,
    AVG(nps_score) as avg_nps,
    AVG(csat_score) as avg_csat,
    ROUND(100.0 * COUNT(CASE WHEN sentiment = 'POSITIVE' THEN 1 END) / COUNT(*), 2) as positive_pct,
    ROUND(100.0 * COUNT(CASE WHEN sentiment = 'NEGATIVE' THEN 1 END) / COUNT(*), 2) as negative_pct
 FROM customer_satisfaction
 GROUP BY category
 ORDER BY avg_nps DESC;
 ```
 ---
 ## Exercise 2.9: Customer Engagement Patterns
 **Objective:** Find most engaged customers in the last 30 days
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    c.customer_id,
    c.first_name,
    c.last_name,
    SUM(em.login_count) as total_logins,
    SUM(em.page_views) as total_page_views,
    SUM(em.time_spent_minutes) as total_time_spent,
    AVG(em.engagement_score) as avg_engagement_score
 FROM customers c
 JOIN engagement_metrics em ON c.customer_id = em.customer_id
 WHERE em.metric_date >= CURRENT_DATE - INTERVAL '30 days'
 GROUP BY c.customer_id, c.first_name, c.last_name
 HAVING AVG(em.engagement_score) > 75
 ORDER BY avg_engagement_score DESC
 LIMIT 20;
 ```
 ---
 ## Exercise 2.10: Churn Prevention Priority List
 **Objective:** Create a priority list for customer retention efforts
 ```sql
 -- Your query here
 ```
 **Hint:** Combine churn risk with customer value
 **Solution:**
 ```sql
 SELECT 
    c.customer_id,
    c.first_name,
    c.last_name,
    c.email,
    clv.clv_score,
    clv.total_revenue,
    cp.churn_probability,
    cp.risk_level,
    cp.days_since_last_transaction,
    (clv.clv_score * cp.churn_probability / 100) as retention_priority_score
 FROM customers c
 JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
 JOIN churn_predictions cp ON c.customer_id = cp.customer_id
 WHERE cp.risk_level IN ('HIGH', 'CRITICAL')
  AND clv.clv_score > 1000
 ORDER BY retention_priority_score DESC
 LIMIT 50;
 ```
 ---
 ## Challenge Exercises
 ### Challenge 2.1: Customer Journey Analysis
 Create a query that shows the customer journey from registration to current status, including:
 - Registration date
 - First transaction date
 - Total transactions
 - Current segment
 - Churn risk
 - Latest satisfaction score
 ### Challenge 2.2: Segment Migration Analysis
 Identify customers who have moved between segments over time (requires historical CLV data)
 ### Challenge 2.3: Engagement Correlation
 Analyze the correlation between engagement scores and satisfaction scores
 ---
 ## Next Steps
 Once you're comfortable with customer analytics, move on to:
 - **Level 3:** Sales & Revenue Analysis
 - **Level 4:** KPI Dashboards & Metrics
@@ -0,0 +1,380 @@
 # Level 3: Sales & Revenue Analysis
 ## Introduction
 Master sales performance analysis, product analytics, and revenue forecasting - core responsibilities for Data Analysts in sales-driven organizations.
 ## Learning Objectives
 - Analyze sales performance vs targets
 - Identify top-performing products
 - Compare sales across channels and regions
 - Calculate revenue metrics
 - Analyze sales trends
 ---
 ## Exercise 3.1: Daily Sales Summary
 **Objective:** Get today's sales summary
 ```sql
 SELECT 
    COUNT(*) as total_sales,
    SUM(total_amount) as total_revenue,
    AVG(total_amount) as avg_sale_value,
    SUM(quantity) as total_units_sold,
    COUNT(DISTINCT product_id) as products_sold
 FROM sales_transactions
 WHERE DATE(sale_date) = CURRENT_DATE;
 ```
 **Expected Result:** Summary of today's sales activity
 ---
 ## Exercise 3.2: Top Products by Revenue
 **Objective:** Find the top 10 products by total revenue
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    p.product_id,
    p.product_name,
    p.product_category,
    p.product_subcategory,
    COUNT(st.transaction_id) as sales_count,
    SUM(st.quantity) as units_sold,
    SUM(st.total_amount) as total_revenue,
    AVG(st.total_amount) as avg_sale_value
 FROM product_catalog p
 JOIN sales_transactions st ON p.product_id = st.product_id
 GROUP BY p.product_id, p.product_name, p.product_category, p.product_subcategory
 ORDER BY total_revenue DESC
 LIMIT 10;
 ```
 ---
 ## Exercise 3.3: Sales by Channel
 **Objective:** Compare sales performance across different channels
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    sales_channel,
    COUNT(*) as transaction_count,
    SUM(total_amount) as total_revenue,
    AVG(total_amount) as avg_transaction_value,
    SUM(quantity) as total_units,
    ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 2) as pct_of_total_transactions
 FROM sales_transactions
 GROUP BY sales_channel
 ORDER BY total_revenue DESC;
 ```
 ---
 ## Exercise 3.4: Regional Performance
 **Objective:** Analyze sales by region
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    region,
    COUNT(*) as sales_count,
    SUM(total_amount) as total_revenue,
    AVG(total_amount) as avg_sale,
    SUM(discount_amount) as total_discounts,
    ROUND(100.0 * SUM(discount_amount) / SUM(total_amount), 2) as discount_rate
 FROM sales_transactions
 GROUP BY region
 ORDER BY total_revenue DESC;
 ```
 ---
 ## Exercise 3.5: Product Category Analysis
 **Objective:** Compare performance across product categories
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    p.product_category,
    COUNT(DISTINCT p.product_id) as product_count,
    COUNT(st.transaction_id) as sales_count,
    SUM(st.total_amount) as total_revenue,
    AVG(st.total_amount) as avg_sale_value,
    SUM(st.quantity) as units_sold,
    AVG(p.margin_percentage) as avg_margin
 FROM product_catalog p
 LEFT JOIN sales_transactions st ON p.product_id = st.product_id
 GROUP BY p.product_category
 ORDER BY total_revenue DESC;
 ```
 ---
 ## Exercise 3.6: Monthly Sales Trend
 **Objective:** Show sales trends over the past 12 months
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    DATE_TRUNC('month', sale_date) as month,
    COUNT(*) as transaction_count,
    SUM(total_amount) as total_revenue,
    AVG(total_amount) as avg_transaction_value,
    SUM(quantity) as units_sold
 FROM sales_transactions
 WHERE sale_date >= CURRENT_DATE - INTERVAL '12 months'
 GROUP BY DATE_TRUNC('month', sale_date)
 ORDER BY month DESC;
 ```
 ---
 ## Exercise 3.7: Sales Performance vs Target
 **Objective:** Compare actual sales to targets
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    st.target_period,
    st.target_category,
    st.target_value,
    COALESCE(sp.actual_value, 0) as actual_value,
    COALESCE(sp.actual_value, 0) - st.target_value as variance,
    ROUND(100.0 * COALESCE(sp.actual_value, 0) / st.target_value, 2) as achievement_pct,
    CASE 
        WHEN COALESCE(sp.actual_value, 0) >= st.target_value THEN 'MET'
        WHEN COALESCE(sp.actual_value, 0) >= st.target_value * 0.9 THEN 'NEAR'
        ELSE 'MISSED'
    END as status
 FROM sales_targets st
 LEFT JOIN sales_performance sp ON st.target_id = sp.target_id
 WHERE st.target_period >= CURRENT_DATE - INTERVAL '6 months'
 ORDER BY st.target_period DESC, st.target_category;
 ```
 ---
 ## Exercise 3.8: Product Profitability
 **Objective:** Calculate profit margins by product
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    p.product_id,
    p.product_name,
    p.product_category,
    p.unit_price,
    p.cost_price,
    p.margin_percentage,
    COUNT(st.transaction_id) as sales_count,
    SUM(st.quantity) as units_sold,
    SUM(st.total_amount) as total_revenue,
    SUM(st.quantity * p.cost_price) as total_cost,
    SUM(st.total_amount) - SUM(st.quantity * p.cost_price) as gross_profit
 FROM product_catalog p
 JOIN sales_transactions st ON p.product_id = st.product_id
 GROUP BY p.product_id, p.product_name, p.product_category, p.unit_price, p.cost_price, p.margin_percentage
 HAVING SUM(st.total_amount) > 0
 ORDER BY gross_profit DESC
 LIMIT 20;
 ```
 ---
 ## Exercise 3.9: Revenue Forecast Accuracy
 **Objective:** Compare forecasted revenue to actual revenue
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    rf.forecast_period,
    rf.forecast_category,
    rf.forecasted_revenue,
    rf.confidence_level,
    ms.total_revenue as actual_revenue,
    (ms.total_revenue - rf.forecasted_revenue) as variance,
    ROUND(100.0 * ABS(ms.total_revenue - rf.forecasted_revenue) / rf.forecasted_revenue, 2) as error_pct
 FROM revenue_forecasts rf
 JOIN monthly_summaries ms ON 
    EXTRACT(MONTH FROM rf.forecast_period) = ms.summary_month AND
    EXTRACT(YEAR FROM rf.forecast_period) = ms.summary_year
 WHERE rf.forecast_period >= CURRENT_DATE - INTERVAL '12 months'
 ORDER BY rf.forecast_period DESC;
 ```
 ---
 ## Exercise 3.10: Sales Velocity Analysis
 **Objective:** Identify fast-moving vs slow-moving products
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 WITH product_sales AS (
    SELECT 
        p.product_id,
        p.product_name,
        p.product_category,
        COUNT(st.transaction_id) as sales_count,
        SUM(st.quantity) as units_sold,
        MIN(st.sale_date) as first_sale,
        MAX(st.sale_date) as last_sale,
        EXTRACT(DAY FROM MAX(st.sale_date) - MIN(st.sale_date)) as days_on_market
    FROM product_catalog p
    LEFT JOIN sales_transactions st ON p.product_id = st.product_id
    WHERE p.is_active = TRUE
    GROUP BY p.product_id, p.product_name, p.product_category
 )
 SELECT 
    product_id,
    product_name,
    product_category,
    sales_count,
    units_sold,
    days_on_market,
    CASE 
        WHEN days_on_market > 0 THEN ROUND(units_sold::DECIMAL / days_on_market, 2)
        ELSE 0
    END as units_per_day,
    CASE 
        WHEN days_on_market > 0 AND (units_sold::DECIMAL / days_on_market) > 10 THEN 'FAST'
        WHEN days_on_market > 0 AND (units_sold::DECIMAL / days_on_market) > 5 THEN 'MEDIUM'
        WHEN days_on_market > 0 THEN 'SLOW'
        ELSE 'NO_SALES'
    END as velocity_category
 FROM product_sales
 ORDER BY units_per_day DESC;
 ```
 ---
 ## Exercise 3.11: Discount Impact Analysis
 **Objective:** Analyze the impact of discounts on sales
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    CASE 
        WHEN discount_amount = 0 THEN 'No Discount'
        WHEN discount_amount / total_amount < 0.1 THEN '< 10%'
        WHEN discount_amount / total_amount < 0.2 THEN '10-20%'
        WHEN discount_amount / total_amount < 0.3 THEN '20-30%'
        ELSE '> 30%'
    END as discount_tier,
    COUNT(*) as transaction_count,
    AVG(total_amount) as avg_sale_value,
    SUM(total_amount) as total_revenue,
    SUM(discount_amount) as total_discounts,
    AVG(quantity) as avg_quantity
 FROM sales_transactions
 GROUP BY discount_tier
 ORDER BY 
    CASE discount_tier
        WHEN 'No Discount' THEN 1
        WHEN '< 10%' THEN 2
        WHEN '10-20%' THEN 3
        WHEN '20-30%' THEN 4
        ELSE 5
    END;
 ```
 ---
 ## Exercise 3.12: Cross-Sell Opportunities
 **Objective:** Find products frequently purchased together
 ```sql
 -- Your query here
 ```
 **Hint:** Use self-join on transaction_id
 **Solution:**
 ```sql
 SELECT 
    p1.product_name as product_1,
    p2.product_name as product_2,
    COUNT(*) as times_purchased_together
 FROM sales_transactions st1
 JOIN sales_transactions st2 ON st1.transaction_id = st2.transaction_id
    AND st1.product_id < st2.product_id
 JOIN product_catalog p1 ON st1.product_id = p1.product_id
 JOIN product_catalog p2 ON st2.product_id = p2.product_id
 GROUP BY p1.product_id, p1.product_name, p2.product_id, p2.product_name
 HAVING COUNT(*) > 10
 ORDER BY times_purchased_together DESC
 LIMIT 20;
 ```
 ---
 ## Challenge Exercises
 ### Challenge 3.1: Sales Seasonality
 Identify seasonal patterns in sales data by analyzing month-over-month and year-over-year trends
 ### Challenge 3.2: Customer Purchase Patterns
 Analyze average time between purchases and identify customers with regular buying patterns
 ### Challenge 3.3: Product Launch Performance
 Compare new product performance (launched in last 6 months) vs established products
 ---
 ## Next Steps
 Once you're comfortable with sales analysis, move on to:
 - **Level 4:** KPI Dashboards & Metrics
 - **Level 6:** Fraud Detection (Advanced)
@@ -0,0 +1,431 @@
 # Level 4: KPI Dashboards & Metrics
 ## Introduction
 Learn to build and maintain KPI dashboards - a critical skill for Data Analysts supporting business decision-making.
 ## Learning Objectives
 - Track key performance indicators
 - Calculate trend metrics
 - Build dashboard queries
 - Monitor data quality
 - Create executive reports
 ---
 ## Exercise 4.1: Current KPI Status
 **Objective:** Get current status of all active KPIs
 ```sql
 SELECT 
    kd.kpi_name,
    kd.kpi_category,
    kd.target_value,
    dm.metric_value as current_value,
    dm.status,
    kd.unit_of_measure,
    ROUND(100.0 * dm.metric_value / kd.target_value, 2) as achievement_pct
 FROM kpi_definitions kd
 LEFT JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE kd.is_active = TRUE
  AND dm.metric_date = CURRENT_DATE
 ORDER BY kd.kpi_category, kd.kpi_name;
 ```
 **Expected Result:** Dashboard view of all KPIs with current status
 ---
 ## Exercise 4.2: KPIs Below Target
 **Objective:** Identify KPIs that are underperforming
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    kd.kpi_name,
    kd.kpi_category,
    kd.target_value,
    dm.metric_value as current_value,
    kd.threshold_warning,
    kd.threshold_critical,
    dm.status,
    (dm.metric_value - kd.target_value) as variance,
    ROUND(100.0 * (dm.metric_value - kd.target_value) / kd.target_value, 2) as variance_pct
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE kd.is_active = TRUE
  AND dm.metric_date = CURRENT_DATE
  AND dm.metric_value < kd.target_value
 ORDER BY variance_pct ASC;
 ```
 ---
 ## Exercise 4.3: KPI Trend Analysis (7 Days)
 **Objective:** Show 7-day trend for critical KPIs
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    kd.kpi_name,
    dm.metric_date,
    dm.metric_value,
    kd.target_value,
    dm.vs_previous_day_percentage,
    dm.status
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE kd.is_active = TRUE
  AND dm.metric_date >= CURRENT_DATE - INTERVAL '7 days'
  AND kd.kpi_category IN ('SALES', 'CUSTOMER')
 ORDER BY kd.kpi_name, dm.metric_date DESC;
 ```
 ---
 ## Exercise 4.4: Monthly Performance Summary
 **Objective:** Aggregate monthly business metrics
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    summary_year,
    summary_month,
    TO_CHAR(TO_DATE(summary_month::TEXT, 'MM'), 'Month') as month_name,
    total_revenue,
    total_transactions,
    total_customers,
    new_customers,
    average_transaction_value,
    ROUND(100.0 * new_customers / total_customers, 2) as new_customer_pct
 FROM monthly_summaries
 WHERE summary_year >= EXTRACT(YEAR FROM CURRENT_DATE) - 1
 ORDER BY summary_year DESC, summary_month DESC;
 ```
 ---
 ## Exercise 4.5: Year-over-Year Comparison
 **Objective:** Compare this year's performance to last year
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 WITH current_year AS (
    SELECT 
        summary_month,
        total_revenue,
        total_transactions,
        new_customers
    FROM monthly_summaries
    WHERE summary_year = EXTRACT(YEAR FROM CURRENT_DATE)
 ),
 previous_year AS (
    SELECT 
        summary_month,
        total_revenue,
        total_transactions,
        new_customers
    FROM monthly_summaries
    WHERE summary_year = EXTRACT(YEAR FROM CURRENT_DATE) - 1
 )
 SELECT 
    cy.summary_month,
    TO_CHAR(TO_DATE(cy.summary_month::TEXT, 'MM'), 'Month') as month_name,
    cy.total_revenue as current_year_revenue,
    py.total_revenue as previous_year_revenue,
    (cy.total_revenue - py.total_revenue) as revenue_change,
    ROUND(100.0 * (cy.total_revenue - py.total_revenue) / py.total_revenue, 2) as revenue_growth_pct,
    cy.total_transactions as current_year_transactions,
    py.total_transactions as previous_year_transactions
 FROM current_year cy
 LEFT JOIN previous_year py ON cy.summary_month = py.summary_month
 ORDER BY cy.summary_month;
 ```
 ---
 ## Exercise 4.6: Dashboard Snapshot
 **Objective:** Create a pre-calculated dashboard snapshot
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 INSERT INTO dashboard_snapshots (
    snapshot_date, dashboard_name, metric_name, metric_value, metric_category
 )
 SELECT 
    CURRENT_DATE as snapshot_date,
    'Executive Dashboard' as dashboard_name,
    kd.kpi_name as metric_name,
    dm.metric_value,
    kd.kpi_category as metric_category
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE kd.is_active = TRUE
  AND dm.metric_date = CURRENT_DATE;
 -- View the snapshot
 SELECT * FROM dashboard_snapshots 
 WHERE snapshot_date = CURRENT_DATE 
 ORDER BY metric_category, metric_name;
 ```
 ---
 ## Exercise 4.7: Trend Detection
 **Objective:** Identify metrics with consistent upward or downward trends
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 WITH trend_data AS (
    SELECT 
        kd.kpi_name,
        kd.kpi_category,
        dm.metric_date,
        dm.metric_value,
        dm.vs_previous_day_percentage,
        ROW_NUMBER() OVER (PARTITION BY kd.kpi_id ORDER BY dm.metric_date DESC) as day_rank
    FROM kpi_definitions kd
    JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
    WHERE dm.metric_date >= CURRENT_DATE - INTERVAL '7 days'
 )
 SELECT 
    kpi_name,
    kpi_category,
    COUNT(*) as days_tracked,
    AVG(vs_previous_day_percentage) as avg_daily_change,
    MIN(metric_value) as min_value,
    MAX(metric_value) as max_value,
    CASE 
        WHEN AVG(vs_previous_day_percentage) > 5 THEN 'STRONG UPWARD'
        WHEN AVG(vs_previous_day_percentage) > 0 THEN 'UPWARD'
        WHEN AVG(vs_previous_day_percentage) > -5 THEN 'DOWNWARD'
        ELSE 'STRONG DOWNWARD'
    END as trend_direction
 FROM trend_data
 GROUP BY kpi_name, kpi_category
 ORDER BY avg_daily_change DESC;
 ```
 ---
 ## Exercise 4.8: Data Quality Monitoring
 **Objective:** Track data quality metrics
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    check_date,
    table_name,
    check_type,
    records_checked,
    records_failed,
    ROUND(100.0 * records_failed / records_checked, 2) as failure_rate,
    status,
    error_details
 FROM data_quality_checks
 WHERE check_date >= CURRENT_DATE - INTERVAL '7 days'
  AND status IN ('WARNING', 'FAILED')
 ORDER BY check_date DESC, failure_rate DESC;
 ```
 ---
 ## Exercise 4.9: Report Execution History
 **Objective:** Monitor report generation and performance
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    rd.report_name,
    rd.report_category,
    re.execution_date,
    re.execution_status,
    re.execution_time_seconds,
    re.records_generated,
    re.file_size_kb
 FROM report_definitions rd
 JOIN report_executions re ON rd.report_id = re.report_id
 WHERE re.execution_date >= CURRENT_DATE - INTERVAL '30 days'
 ORDER BY re.execution_date DESC;
 ```
 ---
 ## Exercise 4.10: Executive Summary Dashboard
 **Objective:** Create a comprehensive executive summary
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 WITH today_metrics AS (
    SELECT 
        SUM(CASE WHEN kd.kpi_category = 'SALES' THEN dm.metric_value ELSE 0 END) as total_sales,
        AVG(CASE WHEN kd.kpi_name = 'Customer Engagement Score' THEN dm.metric_value END) as avg_engagement,
        AVG(CASE WHEN kd.kpi_name = 'Net Promoter Score' THEN dm.metric_value END) as avg_nps,
        COUNT(CASE WHEN dm.status = 'CRITICAL' THEN 1 END) as critical_kpis,
        COUNT(CASE WHEN dm.status = 'WARNING' THEN 1 END) as warning_kpis,
        COUNT(CASE WHEN dm.status = 'ON_TARGET' THEN 1 END) as on_target_kpis
    FROM kpi_definitions kd
    JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
    WHERE dm.metric_date = CURRENT_DATE
 ),
 monthly_summary AS (
    SELECT 
        total_revenue,
        total_transactions,
        new_customers,
        average_transaction_value
    FROM monthly_summaries
    WHERE summary_year = EXTRACT(YEAR FROM CURRENT_DATE)
      AND summary_month = EXTRACT(MONTH FROM CURRENT_DATE)
 )
 SELECT 
    'Executive Summary' as report_title,
    CURRENT_DATE as report_date,
    tm.total_sales,
    tm.avg_engagement,
    tm.avg_nps,
    tm.critical_kpis,
    tm.warning_kpis,
    tm.on_target_kpis,
    ms.total_revenue as mtd_revenue,
    ms.total_transactions as mtd_transactions,
    ms.new_customers as mtd_new_customers
 FROM today_metrics tm
 CROSS JOIN monthly_summary ms;
 ```
 ---
 ## Exercise 4.11: Moving Average Calculation
 **Objective:** Calculate 7-day moving average for key metrics
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 SELECT 
    kd.kpi_name,
    dm.metric_date,
    dm.metric_value,
    AVG(dm.metric_value) OVER (
        PARTITION BY kd.kpi_id 
        ORDER BY dm.metric_date 
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) as moving_avg_7day,
    kd.target_value
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 WHERE kd.kpi_name IN ('Daily Revenue', 'Transactions Per Day', 'Customer Engagement Score')
  AND dm.metric_date >= CURRENT_DATE - INTERVAL '30 days'
 ORDER BY kd.kpi_name, dm.metric_date DESC;
 ```
 ---
 ## Exercise 4.12: Anomaly Detection
 **Objective:** Identify unusual metric values
 ```sql
 -- Your query here
 ```
 **Solution:**
 ```sql
 WITH metric_stats AS (
    SELECT 
        kpi_id,
        AVG(metric_value) as avg_value,
        STDDEV(metric_value) as stddev_value
    FROM daily_metrics
    WHERE metric_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP BY kpi_id
 )
 SELECT 
    kd.kpi_name,
    dm.metric_date,
    dm.metric_value,
    ms.avg_value,
    ms.stddev_value,
    (dm.metric_value - ms.avg_value) / NULLIF(ms.stddev_value, 0) as z_score,
    CASE 
        WHEN ABS((dm.metric_value - ms.avg_value) / NULLIF(ms.stddev_value, 0)) > 3 THEN 'EXTREME ANOMALY'
        WHEN ABS((dm.metric_value - ms.avg_value) / NULLIF(ms.stddev_value, 0)) > 2 THEN 'ANOMALY'
        ELSE 'NORMAL'
    END as anomaly_status
 FROM kpi_definitions kd
 JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
 JOIN metric_stats ms ON kd.kpi_id = ms.kpi_id
 WHERE dm.metric_date >= CURRENT_DATE - INTERVAL '7 days'
  AND ABS((dm.metric_value - ms.avg_value) / NULLIF(ms.stddev_value, 0)) > 2
 ORDER BY ABS((dm.metric_value - ms.avg_value) / NULLIF(ms.stddev_value, 0)) DESC;
 ```
 ---
 ## Challenge Exercises
 ### Challenge 4.1: Custom Dashboard Builder
 Create a flexible query that can generate different dashboard views based on parameters (date range, KPI category, etc.)
 ### Challenge 4.2: Predictive Alerting
 Build a query that predicts which KPIs are likely to miss targets based on current trends
 ### Challenge 4.3: Correlation Analysis
 Identify correlations between different KPIs (e.g., does customer engagement correlate with revenue?)
 ---
 ## Next Steps
 You've now mastered the core Data Analyst skills! Continue with:
 - **Level 5:** Advanced Analytics (Window Functions, CTEs)
 - **Level 6:** Fraud Detection (Advanced Pattern Recognition)
 - Build your own custom dashboards and reports!
@@ -1,6 +1,6 @@
 -- ============================================================================
-- Financial Fraud Detection Database Schema
+-- Business Analytics Database Schema
-- Purpose: Educational SQL learning with realistic fraud investigation scenarios
+-- Purpose: Data Analyst training with routine/semi-routine analysis scenarios
 -- ============================================================================
 -- This script is IDEMPOTENT - it will drop and recreate all objects
 -- ============================================================================
@@ -23,6 +23,27 @@ DROP FUNCTION IF EXISTS check_suspicious_transaction() CASCADE;
 DROP FUNCTION IF EXISTS generate_fraud_score(DECIMAL, BOOLEAN, DECIMAL, INT, BOOLEAN) CASCADE;
 -- Drop tables in reverse dependency order
 -- New analytics tables
 DROP TABLE IF EXISTS report_executions CASCADE;
 DROP TABLE IF EXISTS report_definitions CASCADE;
 DROP TABLE IF EXISTS data_quality_checks CASCADE;
 DROP TABLE IF EXISTS dashboard_snapshots CASCADE;
 DROP TABLE IF EXISTS trend_analysis CASCADE;
 DROP TABLE IF EXISTS monthly_summaries CASCADE;
 DROP TABLE IF EXISTS daily_metrics CASCADE;
 DROP TABLE IF EXISTS kpi_definitions CASCADE;
 DROP TABLE IF EXISTS revenue_forecasts CASCADE;
 DROP TABLE IF EXISTS sales_performance CASCADE;
 DROP TABLE IF EXISTS sales_targets CASCADE;
 DROP TABLE IF EXISTS sales_transactions CASCADE;
 DROP TABLE IF EXISTS product_catalog CASCADE;
 DROP TABLE IF EXISTS engagement_metrics CASCADE;
 DROP TABLE IF EXISTS customer_satisfaction CASCADE;
 DROP TABLE IF EXISTS churn_predictions CASCADE;
 DROP TABLE IF EXISTS customer_lifetime_value CASCADE;
 DROP TABLE IF EXISTS customer_segments CASCADE;
 -- Existing tables
 DROP TABLE IF EXISTS audit_log CASCADE;
 DROP TABLE IF EXISTS suspicious_activity_reports CASCADE;
 DROP TABLE IF EXISTS case_alerts CASCADE;
@@ -475,3 +496,321 @@ COMMENT ON TABLE cards IS 'Payment cards linked to accounts';
 COMMENT ON TABLE devices IS 'Device fingerprints for fraud detection';
 COMMENT ON TABLE login_sessions IS 'Login history for account takeover detection';
 -- ============================================================================
 -- CUSTOMER ANALYTICS TABLES (for routine customer analysis)
 -- ============================================================================
 CREATE TABLE customer_segments (
    segment_id SERIAL PRIMARY KEY,
    segment_name VARCHAR(100) NOT NULL UNIQUE,
    segment_description TEXT,
    criteria_definition JSONB,
    min_clv DECIMAL(15,2),
    max_clv DECIMAL(15,2),
    created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
 );
 CREATE TABLE customer_lifetime_value (
    clv_id SERIAL PRIMARY KEY,
    customer_id INT NOT NULL REFERENCES customers(customer_id),
    calculation_date DATE NOT NULL,
    total_revenue DECIMAL(15,2) DEFAULT 0,
    total_transactions INT DEFAULT 0,
    average_order_value DECIMAL(15,2) DEFAULT 0,
    predicted_future_value DECIMAL(15,2),
    clv_score DECIMAL(10,2),
    segment_id INT REFERENCES customer_segments(segment_id),
    UNIQUE(customer_id, calculation_date)
 );
 CREATE TABLE churn_predictions (
    prediction_id SERIAL PRIMARY KEY,
    customer_id INT NOT NULL REFERENCES customers(customer_id),
    prediction_date DATE NOT NULL,
    churn_probability DECIMAL(5,2) CHECK (churn_probability BETWEEN 0 AND 100),
    risk_level VARCHAR(20) CHECK (risk_level IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
    last_transaction_date DATE,
    days_since_last_transaction INT,
    engagement_score DECIMAL(5,2),
    recommended_action TEXT,
    UNIQUE(customer_id, prediction_date)
 );
 CREATE TABLE customer_satisfaction (
    satisfaction_id SERIAL PRIMARY KEY,
    customer_id INT NOT NULL REFERENCES customers(customer_id),
    survey_date DATE NOT NULL,
    nps_score INT CHECK (nps_score BETWEEN -100 AND 100),
    csat_score DECIMAL(3,2) CHECK (csat_score BETWEEN 1 AND 5),
    feedback_text TEXT,
    category VARCHAR(50) CHECK (category IN ('PRODUCT', 'SERVICE', 'SUPPORT', 'BILLING', 'OTHER')),
    sentiment VARCHAR(20) CHECK (sentiment IN ('POSITIVE', 'NEUTRAL', 'NEGATIVE'))
 );
 CREATE TABLE engagement_metrics (
    metric_id SERIAL PRIMARY KEY,
    customer_id INT NOT NULL REFERENCES customers(customer_id),
    metric_date DATE NOT NULL,
    login_count INT DEFAULT 0,
    page_views INT DEFAULT 0,
    time_spent_minutes INT DEFAULT 0,
    features_used JSONB,
    support_tickets_opened INT DEFAULT 0,
    engagement_score DECIMAL(5,2),
    UNIQUE(customer_id, metric_date)
 );
 -- ============================================================================
 -- SALES & REVENUE ANALYTICS TABLES (for semi-routine sales reporting)
 -- ============================================================================
 CREATE TABLE product_catalog (
    product_id SERIAL PRIMARY KEY,
    product_name VARCHAR(200) NOT NULL,
    product_category VARCHAR(100),
    product_subcategory VARCHAR(100),
    unit_price DECIMAL(15,2) NOT NULL,
    cost_price DECIMAL(15,2),
    margin_percentage DECIMAL(5,2),
    is_active BOOLEAN DEFAULT TRUE,
    launch_date DATE,
    discontinued_date DATE
 );
 CREATE TABLE sales_transactions (
    sale_id SERIAL PRIMARY KEY,
    transaction_id INT REFERENCES transactions(transaction_id),
    product_id INT REFERENCES product_catalog(product_id),
    quantity INT NOT NULL DEFAULT 1,
    unit_price DECIMAL(15,2) NOT NULL,
    discount_amount DECIMAL(15,2) DEFAULT 0,
    tax_amount DECIMAL(15,2) DEFAULT 0,
    total_amount DECIMAL(15,2) NOT NULL,
    sale_date TIMESTAMP NOT NULL,
    sales_channel VARCHAR(50) CHECK (sales_channel IN ('ONLINE', 'STORE', 'PHONE', 'MOBILE_APP')),
    sales_rep_id INT,
    region VARCHAR(100)
 );
 CREATE TABLE sales_targets (
    target_id SERIAL PRIMARY KEY,
    target_period VARCHAR(20) CHECK (target_period IN ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY', 'YEARLY')),
    start_date DATE NOT NULL,
    end_date DATE NOT NULL,
    product_category VARCHAR(100),
    region VARCHAR(100),
    target_revenue DECIMAL(15,2),
    target_units INT,
    target_customers INT,
    created_by VARCHAR(100),
    created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
 );
 CREATE TABLE sales_performance (
    performance_id SERIAL PRIMARY KEY,
    period_date DATE NOT NULL,
    period_type VARCHAR(20) CHECK (period_type IN ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY')),
    product_id INT REFERENCES product_catalog(product_id),
    region VARCHAR(100),
    total_revenue DECIMAL(15,2) DEFAULT 0,
    total_units_sold INT DEFAULT 0,
    total_transactions INT DEFAULT 0,
    unique_customers INT DEFAULT 0,
    average_order_value DECIMAL(15,2),
    vs_target_percentage DECIMAL(5,2),
    UNIQUE(period_date, period_type, product_id, region)
 );
 CREATE TABLE revenue_forecasts (
    forecast_id SERIAL PRIMARY KEY,
    forecast_date DATE NOT NULL,
    forecast_period_start DATE NOT NULL,
    forecast_period_end DATE NOT NULL,
    product_category VARCHAR(100),
    region VARCHAR(100),
    forecasted_revenue DECIMAL(15,2),
    confidence_level VARCHAR(20) CHECK (confidence_level IN ('LOW', 'MEDIUM', 'HIGH')),
    forecast_method VARCHAR(50) CHECK (forecast_method IN ('HISTORICAL', 'TREND', 'SEASONAL', 'ML')),
    actual_revenue DECIMAL(15,2),
    variance_percentage DECIMAL(5,2)
 );
 -- ============================================================================
 -- OPERATIONAL METRICS & KPI TABLES (for dashboard creation)
 -- ============================================================================
 CREATE TABLE kpi_definitions (
    kpi_id SERIAL PRIMARY KEY,
    kpi_name VARCHAR(100) NOT NULL UNIQUE,
    kpi_description TEXT,
    kpi_category VARCHAR(50) CHECK (kpi_category IN ('SALES', 'CUSTOMER', 'OPERATIONAL', 'FINANCIAL')),
    calculation_formula TEXT,
    target_value DECIMAL(15,2),
    threshold_warning DECIMAL(15,2),
    threshold_critical DECIMAL(15,2),
    unit_of_measure VARCHAR(50),
    refresh_frequency VARCHAR(20) CHECK (refresh_frequency IN ('REALTIME', 'HOURLY', 'DAILY', 'WEEKLY')),
    is_active BOOLEAN DEFAULT TRUE
 );
 CREATE TABLE daily_metrics (
    metric_id SERIAL PRIMARY KEY,
    metric_date DATE NOT NULL,
    kpi_id INT NOT NULL REFERENCES kpi_definitions(kpi_id),
    metric_value DECIMAL(15,2),
    vs_previous_day_percentage DECIMAL(5,2),
    vs_previous_week_percentage DECIMAL(5,2),
    vs_previous_month_percentage DECIMAL(5,2),
    status VARCHAR(20) CHECK (status IN ('ON_TARGET', 'WARNING', 'CRITICAL')),
    notes TEXT,
    UNIQUE(metric_date, kpi_id)
 );
 CREATE TABLE monthly_summaries (
    summary_id SERIAL PRIMARY KEY,
    summary_month INT CHECK (summary_month BETWEEN 1 AND 12),
    summary_year INT,
    total_revenue DECIMAL(15,2) DEFAULT 0,
    total_transactions INT DEFAULT 0,
    total_customers INT DEFAULT 0,
    new_customers INT DEFAULT 0,
    churned_customers INT DEFAULT 0,
    average_transaction_value DECIMAL(15,2),
    customer_acquisition_cost DECIMAL(15,2),
    customer_lifetime_value DECIMAL(15,2),
    net_promoter_score DECIMAL(5,2),
    gross_margin_percentage DECIMAL(5,2),
    UNIQUE(summary_month, summary_year)
 );
 CREATE TABLE trend_analysis (
    trend_id SERIAL PRIMARY KEY,
    kpi_id INT NOT NULL REFERENCES kpi_definitions(kpi_id),
    analysis_date DATE NOT NULL,
    period_start DATE NOT NULL,
    period_end DATE NOT NULL,
    trend_direction VARCHAR(20) CHECK (trend_direction IN ('UP', 'DOWN', 'FLAT')),
    trend_strength VARCHAR(20) CHECK (trend_strength IN ('WEAK', 'MODERATE', 'STRONG')),
    moving_average_7day DECIMAL(15,2),
    moving_average_30day DECIMAL(15,2),
    seasonality_detected BOOLEAN DEFAULT FALSE,
    anomalies_detected BOOLEAN DEFAULT FALSE,
    statistical_significance DECIMAL(5,4)
 );
 CREATE TABLE dashboard_snapshots (
    snapshot_id SERIAL PRIMARY KEY,
    dashboard_name VARCHAR(100) NOT NULL,
    snapshot_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    data_payload JSONB,
    refresh_duration_seconds DECIMAL(10,2),
    row_count INT,
    last_updated_by VARCHAR(100)
 );
 -- ============================================================================
 -- BUSINESS INTELLIGENCE & REPORTING TABLES
 -- ============================================================================
 CREATE TABLE report_definitions (
    report_id SERIAL PRIMARY KEY,
    report_name VARCHAR(200) NOT NULL UNIQUE,
    report_description TEXT,
    report_category VARCHAR(100),
    sql_query_template TEXT,
    parameters JSONB,
    output_format VARCHAR(20) CHECK (output_format IN ('PDF', 'EXCEL', 'CSV', 'HTML')),
    schedule_frequency VARCHAR(50),
    recipients TEXT,
    is_active BOOLEAN DEFAULT TRUE,
    created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
 );
 CREATE TABLE report_executions (
    execution_id SERIAL PRIMARY KEY,
    report_id INT NOT NULL REFERENCES report_definitions(report_id),
    execution_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    parameters_used JSONB,
    row_count INT,
    execution_duration_seconds DECIMAL(10,2),
    status VARCHAR(20) CHECK (status IN ('SUCCESS', 'FAILED', 'TIMEOUT')),
    error_message TEXT,
    output_file_path TEXT,
    executed_by VARCHAR(100)
 );
 CREATE TABLE data_quality_checks (
    check_id SERIAL PRIMARY KEY,
    check_name VARCHAR(200) NOT NULL,
    table_name VARCHAR(100) NOT NULL,
    column_name VARCHAR(100),
    check_type VARCHAR(50) CHECK (check_type IN ('NULL_CHECK', 'RANGE_CHECK', 'UNIQUENESS', 'REFERENTIAL_INTEGRITY', 'FORMAT_CHECK')),
    check_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    records_checked INT,
    records_failed INT,
    failure_percentage DECIMAL(5,2),
    status VARCHAR(20) CHECK (status IN ('PASS', 'FAIL', 'WARNING')),
    remediation_notes TEXT
 );
 -- ============================================================================
 -- INDEXES FOR NEW ANALYTICS TABLES
 -- ============================================================================
 -- Customer Analytics indexes
 CREATE INDEX idx_clv_customer ON customer_lifetime_value(customer_id);
 CREATE INDEX idx_clv_date ON customer_lifetime_value(calculation_date);
 CREATE INDEX idx_clv_segment ON customer_lifetime_value(segment_id);
 CREATE INDEX idx_churn_customer ON churn_predictions(customer_id);
 CREATE INDEX idx_churn_risk ON churn_predictions(risk_level);
 CREATE INDEX idx_satisfaction_customer ON customer_satisfaction(customer_id);
 CREATE INDEX idx_engagement_customer ON engagement_metrics(customer_id);
 CREATE INDEX idx_engagement_date ON engagement_metrics(metric_date);
 -- Sales Analytics indexes
 CREATE INDEX idx_sales_trans_product ON sales_transactions(product_id);
 CREATE INDEX idx_sales_trans_date ON sales_transactions(sale_date);
 CREATE INDEX idx_sales_trans_channel ON sales_transactions(sales_channel);
 CREATE INDEX idx_sales_perf_date ON sales_performance(period_date);
 CREATE INDEX idx_sales_perf_product ON sales_performance(product_id);
 CREATE INDEX idx_product_category ON product_catalog(product_category);
 CREATE INDEX idx_product_active ON product_catalog(is_active);
 -- KPI & Metrics indexes
 CREATE INDEX idx_daily_metrics_date ON daily_metrics(metric_date);
 CREATE INDEX idx_daily_metrics_kpi ON daily_metrics(kpi_id);
 CREATE INDEX idx_monthly_summaries_period ON monthly_summaries(summary_year, summary_month);
 CREATE INDEX idx_trend_kpi ON trend_analysis(kpi_id);
 CREATE INDEX idx_trend_date ON trend_analysis(analysis_date);
 -- Reporting indexes
 CREATE INDEX idx_report_exec_report ON report_executions(report_id);
 CREATE INDEX idx_report_exec_timestamp ON report_executions(execution_timestamp);
 CREATE INDEX idx_data_quality_table ON data_quality_checks(table_name);
 CREATE INDEX idx_data_quality_date ON data_quality_checks(check_date);
 -- ============================================================================
 -- COMMENTS FOR NEW TABLES
 -- ============================================================================
 COMMENT ON TABLE customer_segments IS 'Customer segmentation definitions for targeted analysis';
 COMMENT ON TABLE customer_lifetime_value IS 'Customer lifetime value calculations and tracking';
 COMMENT ON TABLE churn_predictions IS 'Customer churn risk predictions for retention analysis';
 COMMENT ON TABLE customer_satisfaction IS 'Customer satisfaction scores and feedback';
 COMMENT ON TABLE engagement_metrics IS 'Customer engagement tracking metrics';
 COMMENT ON TABLE product_catalog IS 'Product master data for sales analysis';
 COMMENT ON TABLE sales_transactions IS 'Detailed sales transaction records';
 COMMENT ON TABLE sales_targets IS 'Sales performance targets and goals';
 COMMENT ON TABLE sales_performance IS 'Aggregated sales performance metrics';
 COMMENT ON TABLE revenue_forecasts IS 'Revenue forecasting and predictions';
 COMMENT ON TABLE kpi_definitions IS 'Master list of tracked KPIs';
 COMMENT ON TABLE daily_metrics IS 'Daily operational metrics for dashboards';
 COMMENT ON TABLE monthly_summaries IS 'Monthly aggregated business summaries';
 COMMENT ON TABLE trend_analysis IS 'Statistical trend analysis results';
 COMMENT ON TABLE dashboard_snapshots IS 'Pre-calculated dashboard data snapshots';
 COMMENT ON TABLE report_definitions IS 'Standard report catalog';
 COMMENT ON TABLE report_executions IS 'Report execution history and logs';
 COMMENT ON TABLE data_quality_checks IS 'Data quality validation tracking';
@@ -5,6 +5,27 @@
 -- ============================================================================
 -- Clear existing reference data (in reverse dependency order)
 -- New analytics tables
 TRUNCATE TABLE report_executions CASCADE;
 TRUNCATE TABLE report_definitions RESTART IDENTITY CASCADE;
 TRUNCATE TABLE data_quality_checks RESTART IDENTITY CASCADE;
 TRUNCATE TABLE dashboard_snapshots RESTART IDENTITY CASCADE;
 TRUNCATE TABLE trend_analysis RESTART IDENTITY CASCADE;
 TRUNCATE TABLE monthly_summaries RESTART IDENTITY CASCADE;
 TRUNCATE TABLE daily_metrics RESTART IDENTITY CASCADE;
 TRUNCATE TABLE kpi_definitions RESTART IDENTITY CASCADE;
 TRUNCATE TABLE revenue_forecasts RESTART IDENTITY CASCADE;
 TRUNCATE TABLE sales_performance RESTART IDENTITY CASCADE;
 TRUNCATE TABLE sales_targets RESTART IDENTITY CASCADE;
 TRUNCATE TABLE sales_transactions RESTART IDENTITY CASCADE;
 TRUNCATE TABLE product_catalog RESTART IDENTITY CASCADE;
 TRUNCATE TABLE engagement_metrics RESTART IDENTITY CASCADE;
 TRUNCATE TABLE customer_satisfaction RESTART IDENTITY CASCADE;
 TRUNCATE TABLE churn_predictions RESTART IDENTITY CASCADE;
 TRUNCATE TABLE customer_lifetime_value RESTART IDENTITY CASCADE;
 TRUNCATE TABLE customer_segments RESTART IDENTITY CASCADE;
 -- Existing tables
 TRUNCATE TABLE suspicious_activity_reports CASCADE;
 TRUNCATE TABLE case_alerts CASCADE;
 TRUNCATE TABLE case_transactions CASCADE;
@@ -254,3 +275,91 @@ COMMENT ON FUNCTION generate_fraud_score IS 'Calculates fraud risk score based o
 COMMENT ON FUNCTION update_account_balance IS 'Automatically updates account balance after transaction';
 COMMENT ON FUNCTION check_suspicious_transaction IS 'Generates alerts for suspicious transactions';
 -- ============================================================================
 -- SEED DATA FOR NEW ANALYTICS TABLES
 -- ============================================================================
 -- Customer Segments
 INSERT INTO customer_segments (segment_name, segment_description, min_clv, max_clv) VALUES
 ('VIP', 'Very Important Person - Highest value customers', 50000, NULL),
 ('High Value', 'High spending customers with strong loyalty', 10000, 49999),
 ('Medium Value', 'Regular customers with moderate spending', 2000, 9999),
 ('Low Value', 'Occasional customers with low spending', 500, 1999),
 ('At Risk', 'Previously active customers showing decline', NULL, NULL),
 ('New Customer', 'Recently acquired customers (< 90 days)', NULL, NULL),
 ('Dormant', 'Inactive customers (> 180 days)', NULL, NULL),
 ('Churned', 'Lost customers who have not transacted in 365+ days', NULL, NULL);
 -- Product Catalog (Sample Products)
 INSERT INTO product_catalog (product_name, product_category, product_subcategory, unit_price, cost_price, margin_percentage, is_active, launch_date) VALUES
 -- Banking Products
 ('Premium Checking Account', 'Banking', 'Checking', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Basic Savings Account', 'Banking', 'Savings', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('High-Yield Savings', 'Banking', 'Savings', 0.00, 0.00, 0.00, TRUE, '2021-06-01'),
 ('Business Checking', 'Banking', 'Business', 15.00, 5.00, 66.67, TRUE, '2020-01-01'),
 ('Student Checking', 'Banking', 'Checking', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 -- Credit Cards
 ('Platinum Credit Card', 'Credit', 'Premium', 99.00, 20.00, 79.80, TRUE, '2020-01-01'),
 ('Gold Credit Card', 'Credit', 'Standard', 49.00, 15.00, 69.39, TRUE, '2020-01-01'),
 ('Cash Back Card', 'Credit', 'Rewards', 0.00, 10.00, -100.00, TRUE, '2021-01-01'),
 ('Travel Rewards Card', 'Credit', 'Rewards', 95.00, 25.00, 73.68, TRUE, '2021-03-01'),
 ('Business Credit Card', 'Credit', 'Business', 75.00, 20.00, 73.33, TRUE, '2020-06-01'),
 -- Loans
 ('Personal Loan', 'Lending', 'Personal', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Auto Loan', 'Lending', 'Auto', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Home Mortgage', 'Lending', 'Mortgage', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Small Business Loan', 'Lending', 'Business', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 -- Investment Products
 ('Index Fund', 'Investment', 'Mutual Funds', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Bond Fund', 'Investment', 'Mutual Funds', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('Retirement Account (IRA)', 'Investment', 'Retirement', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 ('401k Plan', 'Investment', 'Retirement', 0.00, 0.00, 0.00, TRUE, '2020-01-01'),
 -- Services
 ('Wire Transfer', 'Services', 'Transfers', 25.00, 5.00, 80.00, TRUE, '2020-01-01'),
 ('International Transfer', 'Services', 'Transfers', 45.00, 10.00, 77.78, TRUE, '2020-01-01'),
 ('Overdraft Protection', 'Services', 'Protection', 35.00, 5.00, 85.71, TRUE, '2020-01-01'),
 ('Safe Deposit Box', 'Services', 'Security', 75.00, 20.00, 73.33, TRUE, '2020-01-01'),
 ('Financial Advisory', 'Services', 'Advisory', 150.00, 50.00, 66.67, TRUE, '2021-01-01'),
 ('Mobile Banking Premium', 'Services', 'Digital', 9.99, 2.00, 79.98, TRUE, '2022-01-01');
 -- KPI Definitions
 INSERT INTO kpi_definitions (kpi_name, kpi_description, kpi_category, calculation_formula, target_value, threshold_warning, threshold_critical, unit_of_measure, refresh_frequency, is_active) VALUES
 -- Sales KPIs
 ('Daily Revenue', 'Total revenue generated per day', 'SALES', 'SUM(amount) FROM transactions WHERE DATE(transaction_date) = CURRENT_DATE', 500000, 400000, 300000, 'USD', 'DAILY', TRUE),
 ('Monthly Revenue', 'Total revenue for the month', 'SALES', 'SUM(amount) FROM transactions WHERE MONTH(transaction_date) = CURRENT_MONTH', 15000000, 12000000, 10000000, 'USD', 'DAILY', TRUE),
 ('Average Transaction Value', 'Average value per transaction', 'SALES', 'AVG(amount) FROM transactions', 150, 100, 75, 'USD', 'DAILY', TRUE),
 ('Transactions Per Day', 'Number of transactions per day', 'SALES', 'COUNT(*) FROM transactions WHERE DATE(transaction_date) = CURRENT_DATE', 10000, 7500, 5000, 'Count', 'DAILY', TRUE),
 -- Customer KPIs
 ('New Customers', 'New customer acquisitions', 'CUSTOMER', 'COUNT(*) FROM customers WHERE registration_date >= CURRENT_DATE - 30', 1000, 750, 500, 'Count', 'DAILY', TRUE),
 ('Customer Churn Rate', 'Percentage of customers churning', 'CUSTOMER', '(Churned / Total) * 100', 5, 7, 10, 'Percentage', 'WEEKLY', TRUE),
 ('Customer Lifetime Value', 'Average CLV across all customers', 'CUSTOMER', 'AVG(clv_score) FROM customer_lifetime_value', 5000, 4000, 3000, 'USD', 'WEEKLY', TRUE),
 ('Net Promoter Score', 'Customer satisfaction metric', 'CUSTOMER', 'AVG(nps_score) FROM customer_satisfaction', 50, 30, 10, 'Score', 'WEEKLY', TRUE),
 ('Customer Engagement Score', 'Average engagement across customers', 'CUSTOMER', 'AVG(engagement_score) FROM engagement_metrics', 75, 60, 45, 'Score', 'DAILY', TRUE),
 -- Operational KPIs
 ('Transaction Success Rate', 'Percentage of successful transactions', 'OPERATIONAL', '(Successful / Total) * 100', 99, 97, 95, 'Percentage', 'HOURLY', TRUE),
 ('Average Response Time', 'System response time', 'OPERATIONAL', 'AVG(response_time_ms)', 200, 500, 1000, 'Milliseconds', 'REALTIME', TRUE),
 ('Fraud Detection Rate', 'Percentage of fraud caught', 'OPERATIONAL', '(Detected / Total Fraud) * 100', 95, 90, 85, 'Percentage', 'DAILY', TRUE),
 ('Alert Resolution Time', 'Average time to resolve alerts', 'OPERATIONAL', 'AVG(resolution_time_hours)', 24, 48, 72, 'Hours', 'DAILY', TRUE),
 -- Financial KPIs
 ('Gross Margin', 'Overall profit margin', 'FINANCIAL', '((Revenue - Cost) / Revenue) * 100', 75, 65, 55, 'Percentage', 'DAILY', TRUE),
 ('Customer Acquisition Cost', 'Cost to acquire new customer', 'FINANCIAL', 'Marketing Spend / New Customers', 50, 75, 100, 'USD', 'WEEKLY', TRUE),
 ('Return on Investment', 'ROI on marketing campaigns', 'FINANCIAL', '((Revenue - Cost) / Cost) * 100', 300, 200, 100, 'Percentage', 'WEEKLY', TRUE);
 -- Report Definitions
 INSERT INTO report_definitions (report_name, report_description, report_category, output_format, schedule_frequency, is_active) VALUES
 ('Daily Sales Summary', 'Daily sales performance report', 'Sales', 'PDF', 'Daily at 6 AM', TRUE),
 ('Weekly Customer Analytics', 'Customer behavior and segmentation analysis', 'Customer', 'EXCEL', 'Weekly on Monday', TRUE),
 ('Monthly Financial Summary', 'Comprehensive monthly financial report', 'Financial', 'PDF', 'Monthly on 1st', TRUE),
 ('Fraud Detection Report', 'Daily fraud alerts and cases', 'Risk', 'PDF', 'Daily at 8 AM', TRUE),
 ('KPI Dashboard', 'Executive KPI dashboard', 'Executive', 'HTML', 'Daily at 7 AM', TRUE),
 ('Customer Churn Analysis', 'At-risk customer identification', 'Customer', 'EXCEL', 'Weekly on Friday', TRUE),
 ('Product Performance', 'Product sales and profitability analysis', 'Sales', 'EXCEL', 'Monthly on 5th', TRUE),
 ('Data Quality Report', 'Data validation and quality metrics', 'Operations', 'CSV', 'Daily at 5 AM', TRUE);
@@ -3,7 +3,7 @@
 # ============================================================================
 # Database Setup Script - IDEMPOTENT
 # ============================================================================
-# This script sets up the complete fraud detection database
+# This script sets up the complete business analytics database
 # It can be run multiple times safely - it will recreate everything
 # ============================================================================
@@ -19,8 +19,8 @@ NC='\033[0m' # No Color
 # Configuration from environment or defaults
 DB_HOST="${POSTGRES_HOST:-localhost}"
 DB_PORT="${POSTGRES_PORT:-5432}"
-DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_NAME="${POSTGRES_DB:-business_analytics}"
-DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_USER="${POSTGRES_USER:-data_analyst}"
 DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
 # Script directory
@@ -28,7 +28,7 @@ SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
 echo -e "${BLUE}============================================================================${NC}"
-echo -e "${BLUE}Financial Fraud Detection Database - Setup Script${NC}"
+echo -e "${BLUE}Business Analytics Database - Setup Script${NC}"
 echo -e "${BLUE}============================================================================${NC}"
 echo -e "Database: ${GREEN}$DB_NAME${NC}"
 echo -e "Host: ${GREEN}$DB_HOST:$DB_PORT${NC}"
@@ -3,7 +3,7 @@
 # ============================================================================
 # Setup Verification Script
 # ============================================================================
-# Verifies that the fraud detection database is properly set up
+# Verifies that the business analytics database is properly set up
 # ============================================================================
 set -e
@@ -18,12 +18,12 @@ NC='\033[0m' # No Color
 # Configuration
 DB_HOST="${POSTGRES_HOST:-localhost}"
 DB_PORT="${POSTGRES_PORT:-5432}"
-DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_NAME="${POSTGRES_DB:-business_analytics}"
-DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_USER="${POSTGRES_USER:-data_analyst}"
 DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
 echo -e "${BLUE}============================================================================${NC}"
-echo -e "${BLUE}Financial Fraud Detection Database - Verification${NC}"
+echo -e "${BLUE}Business Analytics Database - Verification${NC}"
 echo -e "${BLUE}============================================================================${NC}"
 echo ""
@@ -34,7 +34,7 @@ execute_sql() {
 # Check 1: Docker containers
 echo -e "${YELLOW}[1/10] Checking Docker containers...${NC}"
-if docker ps | grep -q "fraud_detection_db"; then
+if docker ps | grep -q "business_analytics_db"; then
    echo -e "${GREEN}✓ PostgreSQL container is running${NC}"
 else
    echo -e "${RED}✗ PostgreSQL container is not running${NC}"
@@ -42,7 +42,7 @@ else
    exit 1
 fi
-if docker ps | grep -q "fraud_detection_ui"; then
+if docker ps | grep -q "business_analytics_ui"; then
    echo -e "${GREEN}✓ DB-UI container is running${NC}"
 else
    echo -e "${RED}✗ DB-UI container is not running${NC}"