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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
@@ -0,0 +1,358 @@
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# Business Analytics Database - Data Models
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## Overview
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This database supports **Data Analyst** activities including:
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- Routine and semi-routine analysis
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- Dashboard creation and maintenance
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- Business trend identification
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- Statistical analysis and pattern recognition
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- Stakeholder reporting
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---
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## Model 1: Customer Analytics (Routine Analysis)
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### Purpose
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Support customer segmentation, lifetime value analysis, churn prediction, and engagement tracking.
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### Tables
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#### `customer_segments`
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Customer classification for targeted analysis
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```sql
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- segment_id (PK)
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- segment_name (e.g., 'High Value', 'At Risk', 'New Customer')
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- segment_description
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- criteria_definition (JSON)
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- created_date
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- updated_date
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```
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#### `customer_lifetime_value`
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CLV calculations for business insights
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```sql
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- clv_id (PK)
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- customer_id (FK → customers)
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- calculation_date
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- total_revenue
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- total_transactions
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- average_order_value
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- predicted_future_value
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- clv_score
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- segment_id (FK → customer_segments)
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```
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#### `churn_predictions`
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Customer retention analysis
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```sql
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- prediction_id (PK)
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- customer_id (FK → customers)
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- prediction_date
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- churn_probability (0-100)
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- risk_level (LOW, MEDIUM, HIGH, CRITICAL)
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- last_transaction_date
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- days_since_last_transaction
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- engagement_score
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- recommended_action
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```
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#### `customer_satisfaction`
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Satisfaction scores and feedback
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```sql
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- satisfaction_id (PK)
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- customer_id (FK → customers)
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- survey_date
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- nps_score (-100 to 100)
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- csat_score (1-5)
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- feedback_text
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- category (PRODUCT, SERVICE, SUPPORT, etc.)
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- sentiment (POSITIVE, NEUTRAL, NEGATIVE)
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```
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#### `engagement_metrics`
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Customer interaction tracking
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```sql
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- metric_id (PK)
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- customer_id (FK → customers)
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- metric_date
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- login_count
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- page_views
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- time_spent_minutes
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- features_used
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- support_tickets_opened
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- engagement_score
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```
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---
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## Model 2: Sales & Revenue Analytics (Semi-Routine Reporting)
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### Purpose
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Support sales performance tracking, revenue forecasting, and product analysis.
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### Tables
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#### `product_catalog`
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Product master data
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```sql
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- product_id (PK)
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- product_name
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- product_category
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- product_subcategory
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- unit_price
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- cost_price
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- margin_percentage
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- is_active
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- launch_date
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- discontinued_date
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```
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#### `sales_transactions`
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Detailed sales records (extends existing transactions)
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```sql
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- sale_id (PK)
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- transaction_id (FK → transactions)
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- product_id (FK → product_catalog)
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- quantity
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- unit_price
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- discount_amount
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- tax_amount
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- total_amount
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- sale_date
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- sales_channel (ONLINE, STORE, PHONE, MOBILE_APP)
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- sales_rep_id
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- region
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```
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#### `sales_targets`
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Performance goals for tracking
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```sql
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- target_id (PK)
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- target_period (DAILY, WEEKLY, MONTHLY, QUARTERLY, YEARLY)
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- start_date
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- end_date
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- product_category
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- region
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- target_revenue
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- target_units
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- target_customers
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- created_by
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```
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#### `sales_performance`
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Aggregated performance metrics
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```sql
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- performance_id (PK)
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- period_date
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- period_type (DAILY, WEEKLY, MONTHLY, QUARTERLY)
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- product_id (FK → product_catalog)
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- region
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- total_revenue
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- total_units_sold
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- total_transactions
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- unique_customers
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- average_order_value
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- vs_target_percentage
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```
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#### `revenue_forecasts`
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Predictive revenue analysis
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```sql
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- forecast_id (PK)
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- forecast_date
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- forecast_period_start
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- forecast_period_end
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- product_category
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- region
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- forecasted_revenue
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- confidence_level (LOW, MEDIUM, HIGH)
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- forecast_method (HISTORICAL, TREND, SEASONAL, ML)
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- actual_revenue (filled after period ends)
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- variance_percentage
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```
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---
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## Model 3: Operational Metrics & KPIs (Dashboard Creation)
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### Purpose
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Support dashboard creation with pre-calculated KPIs and trend analysis.
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### Tables
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#### `kpi_definitions`
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Master list of tracked KPIs
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```sql
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- kpi_id (PK)
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- kpi_name
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- kpi_description
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- kpi_category (SALES, CUSTOMER, OPERATIONAL, FINANCIAL)
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- calculation_formula
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- target_value
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- threshold_warning
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- threshold_critical
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- unit_of_measure
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- refresh_frequency (REALTIME, HOURLY, DAILY, WEEKLY)
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- is_active
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```
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#### `daily_metrics`
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Daily operational snapshots
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```sql
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- metric_id (PK)
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- metric_date
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- kpi_id (FK → kpi_definitions)
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- metric_value
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- vs_previous_day_percentage
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- vs_previous_week_percentage
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- vs_previous_month_percentage
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- status (ON_TARGET, WARNING, CRITICAL)
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- notes
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```
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#### `monthly_summaries`
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Monthly aggregated data for reporting
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```sql
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- summary_id (PK)
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- summary_month
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- summary_year
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- total_revenue
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- total_transactions
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- total_customers
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- new_customers
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- churned_customers
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- average_transaction_value
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- customer_acquisition_cost
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- customer_lifetime_value
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- net_promoter_score
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- gross_margin_percentage
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```
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#### `trend_analysis`
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Statistical trend tracking
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```sql
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- trend_id (PK)
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- kpi_id (FK → kpi_definitions)
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- analysis_date
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- period_start
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- period_end
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- trend_direction (UP, DOWN, FLAT)
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- trend_strength (WEAK, MODERATE, STRONG)
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- moving_average_7day
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- moving_average_30day
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- seasonality_detected
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- anomalies_detected
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- statistical_significance
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```
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#### `dashboard_snapshots`
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Pre-calculated dashboard data
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```sql
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- snapshot_id (PK)
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- dashboard_name
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- snapshot_timestamp
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- data_payload (JSON)
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- refresh_duration_seconds
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- row_count
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- last_updated_by
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```
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---
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## Model 4: Business Intelligence & Reporting
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### Purpose
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Support ad-hoc analysis and standard report generation.
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### Tables
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#### `report_definitions`
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Catalog of standard reports
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```sql
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- report_id (PK)
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- report_name
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- report_description
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- report_category
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- sql_query_template
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- parameters (JSON)
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- output_format (PDF, EXCEL, CSV, HTML)
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- schedule_frequency
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- recipients
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- is_active
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```
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#### `report_executions`
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Report run history
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```sql
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- execution_id (PK)
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- report_id (FK → report_definitions)
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- execution_timestamp
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- parameters_used (JSON)
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- row_count
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- execution_duration_seconds
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- status (SUCCESS, FAILED, TIMEOUT)
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- error_message
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- output_file_path
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- executed_by
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```
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#### `data_quality_checks`
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Data validation tracking
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```sql
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- check_id (PK)
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- check_name
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- table_name
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- column_name
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- check_type (NULL_CHECK, RANGE_CHECK, UNIQUENESS, REFERENTIAL_INTEGRITY)
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- check_date
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- records_checked
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- records_failed
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- failure_percentage
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- status (PASS, FAIL, WARNING)
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- remediation_notes
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```
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---
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## Relationships to Existing Models
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### Integration Points
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1. **Customer Analytics** ← links to existing `customers` table
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2. **Sales Transactions** ← extends existing `transactions` table
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3. **Engagement Metrics** ← links to `login_sessions` and `transactions`
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4. **Churn Predictions** ← analyzes `transactions` and `accounts`
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5. **KPIs** ← aggregates from multiple existing tables
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---
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## Use Cases for Data Analyst Role
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### Routine Analysis
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- Daily sales reports
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- Customer segment updates
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- KPI dashboard refreshes
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- Data quality checks
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### Semi-Routine Analysis
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- Monthly trend analysis
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- Churn prediction updates
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- Revenue forecasting
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- Performance vs. targets
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### Ad-Hoc Analysis
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- Customer cohort analysis
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- Product performance deep-dives
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- Seasonal pattern identification
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- Anomaly investigation
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---
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## Next Steps
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1. ✅ Design complete
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2. ⏳ Implement schema in `schema/01-create-tables.sql`
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3. ⏳ Update data generation scripts
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4. ⏳ Create SQL exercises for each model
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5. ⏳ Update documentation
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+64
-33
@@ -19,7 +19,7 @@ docker-compose up -d
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docker-compose ps
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```
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You should see both `fraud_detection_db` and `fraud_detection_ui` running.
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You should see both `business_analytics_db` and `business_analytics_ui` running.
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---
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@@ -34,9 +34,9 @@ chmod +x scripts/setup-database.sh
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```
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**What this does:**
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- Creates all 20+ tables
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- Creates all 39 tables across 4 business models
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- Sets up indexes and constraints
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- Loads reference data (countries, merchant categories, etc.)
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- Loads reference data (countries, merchant categories, customer segments, products, KPIs, etc.)
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**Expected output:**
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```
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@@ -63,6 +63,9 @@ chmod +x data/generate_data.sh
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- Creates 150,000 accounts
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- Generates 5,000,000 transactions
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- Creates fraud patterns and alerts
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- Generates customer analytics (CLV, churn, satisfaction)
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- Creates sales data (1M sales records)
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- Generates KPI metrics (90 days of daily metrics)
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**⏱️ Time estimate:**
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- Fast machine (SSD, 16GB RAM): ~15 minutes
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@@ -72,10 +75,16 @@ chmod +x data/generate_data.sh
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**You can monitor progress:**
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The script shows progress for each step:
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```
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[1/9] Loading geographic reference data...
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[2/9] Generating Customers...
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[3/9] Generating Accounts...
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[1/15] Loading geographic reference data...
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[2/15] Generating Customers...
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[3/15] Generating Accounts...
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...
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[10/15] Generating Customer Lifetime Value data...
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[11/15] Generating Churn Predictions...
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[12/15] Generating Customer Satisfaction data...
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[13/15] Generating Sales Transactions...
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[14/15] Generating Daily Metrics...
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[15/15] Generating Monthly Summaries...
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```
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---
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@@ -98,7 +107,7 @@ The script shows progress for each step:
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#### Option B: Command Line (psql)
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```bash
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docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
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docker exec -it business_analytics_db psql -U data_analyst -d business_analytics
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```
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**Quick commands:**
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@@ -122,8 +131,8 @@ SELECT COUNT(*) FROM transactions;
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```
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Host: localhost
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Port: 5432
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Database: fraud_detection
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Username: fraud_analyst
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Database: business_analytics
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Username: data_analyst
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Password: SecurePass123!
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```
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@@ -146,43 +155,65 @@ SELECT COUNT(*) FROM customers;
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-- How many transactions?
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SELECT COUNT(*) FROM transactions;
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-- How many fraud alerts?
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SELECT COUNT(*) FROM alerts WHERE status = 'OPEN';
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-- How many sales records?
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SELECT COUNT(*) FROM sales_transactions;
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-- How many KPIs are being tracked?
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SELECT COUNT(*) FROM kpi_definitions WHERE is_active = TRUE;
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```
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### 2. Find High-Risk Customers
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### 2. Customer Analytics: High-Value Customers
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```sql
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SELECT
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customer_id,
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first_name,
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last_name,
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email,
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risk_score
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FROM customers
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WHERE risk_score > 80
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ORDER BY risk_score DESC
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SELECT
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c.customer_id,
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c.first_name,
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c.last_name,
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clv.clv_score,
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cs.segment_name
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FROM customers c
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JOIN customer_lifetime_value clv ON c.customer_id = clv.customer_id
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JOIN customer_segments cs ON clv.segment_id = cs.segment_id
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WHERE cs.segment_name IN ('VIP', 'High Value')
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ORDER BY clv.clv_score DESC
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LIMIT 10;
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```
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### 3. View Recent Transactions
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### 3. Sales Analytics: Top Products
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```sql
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SELECT
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transaction_id,
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account_id,
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amount,
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transaction_date,
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is_flagged
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FROM transactions
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ORDER BY transaction_date DESC
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LIMIT 20;
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SELECT
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p.product_name,
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p.product_category,
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COUNT(st.transaction_id) as sales_count,
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SUM(st.total_amount) as total_revenue
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FROM product_catalog p
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JOIN sales_transactions st ON p.product_id = st.product_id
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GROUP BY p.product_id, p.product_name, p.product_category
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ORDER BY total_revenue DESC
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LIMIT 10;
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```
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### 4. Find Flagged Transactions
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### 4. KPI Dashboard: Current Status
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```sql
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SELECT
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SELECT
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kd.kpi_name,
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kd.kpi_category,
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dm.metric_value,
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kd.target_value,
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dm.status
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FROM kpi_definitions kd
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JOIN daily_metrics dm ON kd.kpi_id = dm.kpi_id
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WHERE dm.metric_date = CURRENT_DATE
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AND kd.is_active = TRUE
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ORDER BY kd.kpi_category, kd.kpi_name;
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```
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### 5. Fraud Detection: Flagged Transactions
|
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|
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```sql
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SELECT
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t.transaction_id,
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t.amount,
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t.fraud_score,
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@@ -0,0 +1,266 @@
|
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# 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!** 🎉
|
||||
|
||||
Reference in New Issue
Block a user