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Expand to Business Analytics: Add Customer, Sales, and KPI models
Major expansion from fraud detection to comprehensive business analytics: DATABASE CHANGES: - Renamed database from 'fraud_detection' to 'business_analytics' - Renamed user from 'fraud_analyst' to 'data_analyst' - Expanded from 20 to 39 tables across 4 business models NEW MODELS (19 tables): 1. Customer Analytics (5 tables): - customer_segments, customer_lifetime_value, churn_predictions - customer_satisfaction, engagement_metrics 2. Sales & Revenue Analytics (6 tables): - product_catalog, sales_transactions, sales_targets - sales_performance, revenue_forecasts 3. KPI & Metrics (8 tables): - kpi_definitions, daily_metrics, monthly_summaries - trend_analysis, dashboard_snapshots - report_definitions, report_executions, data_quality_checks DATA GENERATION: - Extended generate_data.sh with 6 new steps (now 15 total) - Added CLV calculations for all customers - Added churn predictions based on transaction recency - Added 30K customer satisfaction surveys - Added 1M sales transactions linked to 24 products - Added 90 days of daily KPI metrics - Added 24 months of business summaries SQL EXERCISES (3 new levels): - Level 2: Customer Analytics (10 exercises + 3 challenges) - Level 3: Sales & Revenue Analysis (12 exercises + 3 challenges) - Level 4: KPI Dashboards & Metrics (12 exercises + 3 challenges) DOCUMENTATION: - Updated README.md with business analytics focus - Updated QUICKSTART.md with new data generation steps - Updated SETUP_COMPLETE.md with 39-table architecture - Added DATA_MODELS.md with complete model specifications - Added WHATS_NEW.md with migration guide SEED DATA: - Added 8 customer segments (VIP, High Value, etc.) - Added 24 products across 5 categories - Added 16 KPI definitions across 4 categories - Added 8 standard report definitions All changes maintain idempotency and backward compatibility with existing fraud detection functionality.
This commit is contained in:
Alphaeus Mote
+341
-2
@@ -1,6 +1,6 @@
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-- ============================================================================
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-- Financial Fraud Detection Database Schema
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-- Purpose: Educational SQL learning with realistic fraud investigation scenarios
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-- Business Analytics Database Schema
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-- Purpose: Data Analyst training with routine/semi-routine analysis scenarios
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-- ============================================================================
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-- This script is IDEMPOTENT - it will drop and recreate all objects
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-- ============================================================================
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@@ -23,6 +23,27 @@ DROP FUNCTION IF EXISTS check_suspicious_transaction() CASCADE;
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DROP FUNCTION IF EXISTS generate_fraud_score(DECIMAL, BOOLEAN, DECIMAL, INT, BOOLEAN) CASCADE;
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-- Drop tables in reverse dependency order
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-- New analytics tables
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DROP TABLE IF EXISTS report_executions CASCADE;
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DROP TABLE IF EXISTS report_definitions CASCADE;
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DROP TABLE IF EXISTS data_quality_checks CASCADE;
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DROP TABLE IF EXISTS dashboard_snapshots CASCADE;
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DROP TABLE IF EXISTS trend_analysis CASCADE;
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DROP TABLE IF EXISTS monthly_summaries CASCADE;
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DROP TABLE IF EXISTS daily_metrics CASCADE;
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DROP TABLE IF EXISTS kpi_definitions CASCADE;
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DROP TABLE IF EXISTS revenue_forecasts CASCADE;
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DROP TABLE IF EXISTS sales_performance CASCADE;
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DROP TABLE IF EXISTS sales_targets CASCADE;
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DROP TABLE IF EXISTS sales_transactions CASCADE;
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DROP TABLE IF EXISTS product_catalog CASCADE;
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DROP TABLE IF EXISTS engagement_metrics CASCADE;
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DROP TABLE IF EXISTS customer_satisfaction CASCADE;
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DROP TABLE IF EXISTS churn_predictions CASCADE;
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DROP TABLE IF EXISTS customer_lifetime_value CASCADE;
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DROP TABLE IF EXISTS customer_segments CASCADE;
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-- Existing tables
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DROP TABLE IF EXISTS audit_log CASCADE;
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DROP TABLE IF EXISTS suspicious_activity_reports CASCADE;
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DROP TABLE IF EXISTS case_alerts CASCADE;
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@@ -475,3 +496,321 @@ COMMENT ON TABLE cards IS 'Payment cards linked to accounts';
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COMMENT ON TABLE devices IS 'Device fingerprints for fraud detection';
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COMMENT ON TABLE login_sessions IS 'Login history for account takeover detection';
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-- ============================================================================
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-- CUSTOMER ANALYTICS TABLES (for routine customer analysis)
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-- ============================================================================
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CREATE TABLE customer_segments (
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segment_id SERIAL PRIMARY KEY,
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segment_name VARCHAR(100) NOT NULL UNIQUE,
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segment_description TEXT,
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criteria_definition JSONB,
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min_clv DECIMAL(15,2),
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max_clv DECIMAL(15,2),
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created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE customer_lifetime_value (
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clv_id SERIAL PRIMARY KEY,
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customer_id INT NOT NULL REFERENCES customers(customer_id),
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calculation_date DATE NOT NULL,
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total_revenue DECIMAL(15,2) DEFAULT 0,
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total_transactions INT DEFAULT 0,
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average_order_value DECIMAL(15,2) DEFAULT 0,
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predicted_future_value DECIMAL(15,2),
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clv_score DECIMAL(10,2),
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segment_id INT REFERENCES customer_segments(segment_id),
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UNIQUE(customer_id, calculation_date)
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);
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CREATE TABLE churn_predictions (
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prediction_id SERIAL PRIMARY KEY,
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customer_id INT NOT NULL REFERENCES customers(customer_id),
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prediction_date DATE NOT NULL,
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churn_probability DECIMAL(5,2) CHECK (churn_probability BETWEEN 0 AND 100),
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risk_level VARCHAR(20) CHECK (risk_level IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
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last_transaction_date DATE,
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days_since_last_transaction INT,
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engagement_score DECIMAL(5,2),
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recommended_action TEXT,
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UNIQUE(customer_id, prediction_date)
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);
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CREATE TABLE customer_satisfaction (
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satisfaction_id SERIAL PRIMARY KEY,
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customer_id INT NOT NULL REFERENCES customers(customer_id),
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survey_date DATE NOT NULL,
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nps_score INT CHECK (nps_score BETWEEN -100 AND 100),
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csat_score DECIMAL(3,2) CHECK (csat_score BETWEEN 1 AND 5),
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feedback_text TEXT,
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category VARCHAR(50) CHECK (category IN ('PRODUCT', 'SERVICE', 'SUPPORT', 'BILLING', 'OTHER')),
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sentiment VARCHAR(20) CHECK (sentiment IN ('POSITIVE', 'NEUTRAL', 'NEGATIVE'))
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);
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CREATE TABLE engagement_metrics (
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metric_id SERIAL PRIMARY KEY,
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customer_id INT NOT NULL REFERENCES customers(customer_id),
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metric_date DATE NOT NULL,
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login_count INT DEFAULT 0,
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page_views INT DEFAULT 0,
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time_spent_minutes INT DEFAULT 0,
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features_used JSONB,
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support_tickets_opened INT DEFAULT 0,
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engagement_score DECIMAL(5,2),
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UNIQUE(customer_id, metric_date)
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);
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-- ============================================================================
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-- SALES & REVENUE ANALYTICS TABLES (for semi-routine sales reporting)
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-- ============================================================================
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CREATE TABLE product_catalog (
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product_id SERIAL PRIMARY KEY,
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product_name VARCHAR(200) NOT NULL,
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product_category VARCHAR(100),
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product_subcategory VARCHAR(100),
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unit_price DECIMAL(15,2) NOT NULL,
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cost_price DECIMAL(15,2),
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margin_percentage DECIMAL(5,2),
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is_active BOOLEAN DEFAULT TRUE,
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launch_date DATE,
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discontinued_date DATE
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);
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CREATE TABLE sales_transactions (
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sale_id SERIAL PRIMARY KEY,
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transaction_id INT REFERENCES transactions(transaction_id),
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product_id INT REFERENCES product_catalog(product_id),
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quantity INT NOT NULL DEFAULT 1,
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unit_price DECIMAL(15,2) NOT NULL,
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discount_amount DECIMAL(15,2) DEFAULT 0,
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tax_amount DECIMAL(15,2) DEFAULT 0,
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total_amount DECIMAL(15,2) NOT NULL,
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sale_date TIMESTAMP NOT NULL,
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sales_channel VARCHAR(50) CHECK (sales_channel IN ('ONLINE', 'STORE', 'PHONE', 'MOBILE_APP')),
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sales_rep_id INT,
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region VARCHAR(100)
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);
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CREATE TABLE sales_targets (
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target_id SERIAL PRIMARY KEY,
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target_period VARCHAR(20) CHECK (target_period IN ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY', 'YEARLY')),
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start_date DATE NOT NULL,
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end_date DATE NOT NULL,
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product_category VARCHAR(100),
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region VARCHAR(100),
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target_revenue DECIMAL(15,2),
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target_units INT,
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target_customers INT,
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created_by VARCHAR(100),
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created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE sales_performance (
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performance_id SERIAL PRIMARY KEY,
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period_date DATE NOT NULL,
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period_type VARCHAR(20) CHECK (period_type IN ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY')),
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product_id INT REFERENCES product_catalog(product_id),
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region VARCHAR(100),
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total_revenue DECIMAL(15,2) DEFAULT 0,
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total_units_sold INT DEFAULT 0,
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total_transactions INT DEFAULT 0,
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unique_customers INT DEFAULT 0,
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average_order_value DECIMAL(15,2),
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vs_target_percentage DECIMAL(5,2),
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UNIQUE(period_date, period_type, product_id, region)
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);
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CREATE TABLE revenue_forecasts (
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forecast_id SERIAL PRIMARY KEY,
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forecast_date DATE NOT NULL,
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forecast_period_start DATE NOT NULL,
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forecast_period_end DATE NOT NULL,
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product_category VARCHAR(100),
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region VARCHAR(100),
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forecasted_revenue DECIMAL(15,2),
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confidence_level VARCHAR(20) CHECK (confidence_level IN ('LOW', 'MEDIUM', 'HIGH')),
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forecast_method VARCHAR(50) CHECK (forecast_method IN ('HISTORICAL', 'TREND', 'SEASONAL', 'ML')),
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actual_revenue DECIMAL(15,2),
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variance_percentage DECIMAL(5,2)
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);
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-- ============================================================================
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-- OPERATIONAL METRICS & KPI TABLES (for dashboard creation)
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-- ============================================================================
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CREATE TABLE kpi_definitions (
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kpi_id SERIAL PRIMARY KEY,
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kpi_name VARCHAR(100) NOT NULL UNIQUE,
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kpi_description TEXT,
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kpi_category VARCHAR(50) CHECK (kpi_category IN ('SALES', 'CUSTOMER', 'OPERATIONAL', 'FINANCIAL')),
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calculation_formula TEXT,
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target_value DECIMAL(15,2),
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threshold_warning DECIMAL(15,2),
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threshold_critical DECIMAL(15,2),
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unit_of_measure VARCHAR(50),
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refresh_frequency VARCHAR(20) CHECK (refresh_frequency IN ('REALTIME', 'HOURLY', 'DAILY', 'WEEKLY')),
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is_active BOOLEAN DEFAULT TRUE
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);
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CREATE TABLE daily_metrics (
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metric_id SERIAL PRIMARY KEY,
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metric_date DATE NOT NULL,
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kpi_id INT NOT NULL REFERENCES kpi_definitions(kpi_id),
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metric_value DECIMAL(15,2),
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vs_previous_day_percentage DECIMAL(5,2),
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vs_previous_week_percentage DECIMAL(5,2),
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vs_previous_month_percentage DECIMAL(5,2),
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status VARCHAR(20) CHECK (status IN ('ON_TARGET', 'WARNING', 'CRITICAL')),
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notes TEXT,
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UNIQUE(metric_date, kpi_id)
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);
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CREATE TABLE monthly_summaries (
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summary_id SERIAL PRIMARY KEY,
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summary_month INT CHECK (summary_month BETWEEN 1 AND 12),
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summary_year INT,
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total_revenue DECIMAL(15,2) DEFAULT 0,
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total_transactions INT DEFAULT 0,
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total_customers INT DEFAULT 0,
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new_customers INT DEFAULT 0,
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churned_customers INT DEFAULT 0,
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average_transaction_value DECIMAL(15,2),
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customer_acquisition_cost DECIMAL(15,2),
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customer_lifetime_value DECIMAL(15,2),
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net_promoter_score DECIMAL(5,2),
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gross_margin_percentage DECIMAL(5,2),
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UNIQUE(summary_month, summary_year)
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);
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CREATE TABLE trend_analysis (
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trend_id SERIAL PRIMARY KEY,
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kpi_id INT NOT NULL REFERENCES kpi_definitions(kpi_id),
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analysis_date DATE NOT NULL,
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period_start DATE NOT NULL,
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period_end DATE NOT NULL,
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trend_direction VARCHAR(20) CHECK (trend_direction IN ('UP', 'DOWN', 'FLAT')),
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trend_strength VARCHAR(20) CHECK (trend_strength IN ('WEAK', 'MODERATE', 'STRONG')),
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moving_average_7day DECIMAL(15,2),
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moving_average_30day DECIMAL(15,2),
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seasonality_detected BOOLEAN DEFAULT FALSE,
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anomalies_detected BOOLEAN DEFAULT FALSE,
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statistical_significance DECIMAL(5,4)
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);
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CREATE TABLE dashboard_snapshots (
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snapshot_id SERIAL PRIMARY KEY,
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dashboard_name VARCHAR(100) NOT NULL,
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snapshot_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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data_payload JSONB,
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refresh_duration_seconds DECIMAL(10,2),
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row_count INT,
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last_updated_by VARCHAR(100)
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);
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-- ============================================================================
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-- BUSINESS INTELLIGENCE & REPORTING TABLES
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-- ============================================================================
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CREATE TABLE report_definitions (
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report_id SERIAL PRIMARY KEY,
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report_name VARCHAR(200) NOT NULL UNIQUE,
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report_description TEXT,
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report_category VARCHAR(100),
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sql_query_template TEXT,
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parameters JSONB,
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output_format VARCHAR(20) CHECK (output_format IN ('PDF', 'EXCEL', 'CSV', 'HTML')),
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schedule_frequency VARCHAR(50),
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recipients TEXT,
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is_active BOOLEAN DEFAULT TRUE,
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created_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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);
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CREATE TABLE report_executions (
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execution_id SERIAL PRIMARY KEY,
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report_id INT NOT NULL REFERENCES report_definitions(report_id),
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execution_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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parameters_used JSONB,
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row_count INT,
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execution_duration_seconds DECIMAL(10,2),
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status VARCHAR(20) CHECK (status IN ('SUCCESS', 'FAILED', 'TIMEOUT')),
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error_message TEXT,
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output_file_path TEXT,
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executed_by VARCHAR(100)
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);
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CREATE TABLE data_quality_checks (
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check_id SERIAL PRIMARY KEY,
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check_name VARCHAR(200) NOT NULL,
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table_name VARCHAR(100) NOT NULL,
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column_name VARCHAR(100),
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check_type VARCHAR(50) CHECK (check_type IN ('NULL_CHECK', 'RANGE_CHECK', 'UNIQUENESS', 'REFERENTIAL_INTEGRITY', 'FORMAT_CHECK')),
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check_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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records_checked INT,
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records_failed INT,
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failure_percentage DECIMAL(5,2),
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status VARCHAR(20) CHECK (status IN ('PASS', 'FAIL', 'WARNING')),
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remediation_notes TEXT
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);
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-- ============================================================================
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-- INDEXES FOR NEW ANALYTICS TABLES
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-- ============================================================================
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-- Customer Analytics indexes
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CREATE INDEX idx_clv_customer ON customer_lifetime_value(customer_id);
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CREATE INDEX idx_clv_date ON customer_lifetime_value(calculation_date);
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CREATE INDEX idx_clv_segment ON customer_lifetime_value(segment_id);
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CREATE INDEX idx_churn_customer ON churn_predictions(customer_id);
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CREATE INDEX idx_churn_risk ON churn_predictions(risk_level);
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CREATE INDEX idx_satisfaction_customer ON customer_satisfaction(customer_id);
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CREATE INDEX idx_engagement_customer ON engagement_metrics(customer_id);
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CREATE INDEX idx_engagement_date ON engagement_metrics(metric_date);
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-- Sales Analytics indexes
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CREATE INDEX idx_sales_trans_product ON sales_transactions(product_id);
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CREATE INDEX idx_sales_trans_date ON sales_transactions(sale_date);
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CREATE INDEX idx_sales_trans_channel ON sales_transactions(sales_channel);
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CREATE INDEX idx_sales_perf_date ON sales_performance(period_date);
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CREATE INDEX idx_sales_perf_product ON sales_performance(product_id);
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CREATE INDEX idx_product_category ON product_catalog(product_category);
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CREATE INDEX idx_product_active ON product_catalog(is_active);
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-- KPI & Metrics indexes
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CREATE INDEX idx_daily_metrics_date ON daily_metrics(metric_date);
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CREATE INDEX idx_daily_metrics_kpi ON daily_metrics(kpi_id);
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CREATE INDEX idx_monthly_summaries_period ON monthly_summaries(summary_year, summary_month);
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CREATE INDEX idx_trend_kpi ON trend_analysis(kpi_id);
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CREATE INDEX idx_trend_date ON trend_analysis(analysis_date);
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-- Reporting indexes
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CREATE INDEX idx_report_exec_report ON report_executions(report_id);
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CREATE INDEX idx_report_exec_timestamp ON report_executions(execution_timestamp);
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CREATE INDEX idx_data_quality_table ON data_quality_checks(table_name);
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CREATE INDEX idx_data_quality_date ON data_quality_checks(check_date);
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-- ============================================================================
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-- COMMENTS FOR NEW TABLES
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-- ============================================================================
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COMMENT ON TABLE customer_segments IS 'Customer segmentation definitions for targeted analysis';
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COMMENT ON TABLE customer_lifetime_value IS 'Customer lifetime value calculations and tracking';
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COMMENT ON TABLE churn_predictions IS 'Customer churn risk predictions for retention analysis';
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COMMENT ON TABLE customer_satisfaction IS 'Customer satisfaction scores and feedback';
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COMMENT ON TABLE engagement_metrics IS 'Customer engagement tracking metrics';
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COMMENT ON TABLE product_catalog IS 'Product master data for sales analysis';
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COMMENT ON TABLE sales_transactions IS 'Detailed sales transaction records';
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COMMENT ON TABLE sales_targets IS 'Sales performance targets and goals';
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COMMENT ON TABLE sales_performance IS 'Aggregated sales performance metrics';
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COMMENT ON TABLE revenue_forecasts IS 'Revenue forecasting and predictions';
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COMMENT ON TABLE kpi_definitions IS 'Master list of tracked KPIs';
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COMMENT ON TABLE daily_metrics IS 'Daily operational metrics for dashboards';
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COMMENT ON TABLE monthly_summaries IS 'Monthly aggregated business summaries';
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COMMENT ON TABLE trend_analysis IS 'Statistical trend analysis results';
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COMMENT ON TABLE dashboard_snapshots IS 'Pre-calculated dashboard data snapshots';
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COMMENT ON TABLE report_definitions IS 'Standard report catalog';
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COMMENT ON TABLE report_executions IS 'Report execution history and logs';
|
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COMMENT ON TABLE data_quality_checks IS 'Data quality validation tracking';
|
||||
|
||||
|
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@@ -5,6 +5,27 @@
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-- ============================================================================
|
||||
|
||||
-- Clear existing reference data (in reverse dependency order)
|
||||
-- New analytics tables
|
||||
TRUNCATE TABLE report_executions CASCADE;
|
||||
TRUNCATE TABLE report_definitions RESTART IDENTITY CASCADE;
|
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TRUNCATE TABLE data_quality_checks RESTART IDENTITY CASCADE;
|
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TRUNCATE TABLE dashboard_snapshots RESTART IDENTITY CASCADE;
|
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TRUNCATE TABLE trend_analysis RESTART IDENTITY CASCADE;
|
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TRUNCATE TABLE monthly_summaries RESTART IDENTITY CASCADE;
|
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TRUNCATE TABLE daily_metrics RESTART IDENTITY CASCADE;
|
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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;
|
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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;
|
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TRUNCATE TABLE customer_segments RESTART IDENTITY CASCADE;
|
||||
|
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-- 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);
|
||||
|
||||
|
||||
Reference in New Issue
Block a user