┌─────────────────────────────────────────────────────────────────────────────┐
│                    DATA-DRIVEN BANKING MATURITY MODEL                     │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  Level 1           Level 2           Level 3           Level 4             │
│  ┌─────────┐      ┌─────────┐      ┌─────────┐      ┌─────────┐          │
│  │ Data-   │      │ Data-   │      │ Data-   │      │ Data-   │          │
│  │ Aware   │ ──→ │ Enabled │ ──→ │ Informed │ ──→ │ Native  │          │
│  └─────────┘      └─────────┘      └─────────┘      └─────────┘          │
│                                                                             │
│  • Data recognised│ • Data accessible │ • Data used in  │ • Data is core   │
│  • Ad-hoc analysis │ • Basic reporting │ • decision-making│ • AI-driven      │
│  • Siloed data    │ • Dashboards     │ • Predictive    │ • Continuous     │
│                    │                  │   analytics     │   innovation     │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 4, LESSON 1: DATA-DRIVEN BANKING – STRATEGY AND CULTURE
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("DATA-DRIVEN BANKING – STRATEGY AND CULTURE")
print("="*70)

# ----------------------------------------------------------------
# PART A: DATA MATURITY ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Data Maturity Assessment")
print("-"*60)

maturity_dimensions = {
    'Data Strategy': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Data Governance': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Data Quality': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Data Architecture': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Analytics Capability': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Data Culture': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'Data Literacy': {'Current Score': 2, 'Target Score': 4, 'Priority': 'High'},
    'AI/ML Capability': {'Current Score': 2, 'Target Score': 4, 'Priority': 'Medium'},
    'Data Monetisation': {'Current Score': 1, 'Target Score': 3, 'Priority': 'Medium'},
    'Data Security': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

maturity_df = pd.DataFrame(maturity_dimensions).T
print("Data Maturity Assessment:")
print(maturity_df)

# Visualise
fig, ax = plt.subplots(figsize=(10, 8))
dimensions = list(maturity_df.index)
current = maturity_df['Current Score'].tolist()
target = maturity_df['Target Score'].tolist()

x = np.arange(len(dimensions))
width = 0.35

ax.barh(x - width/2, current, width, label='Current', color='blue', alpha=0.7)
ax.barh(x + width/2, target, width, label='Target', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(dimensions)
ax.set_xlabel('Maturity Score (1-5)')
ax.set_title('Data Maturity Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('data_maturity.png', dpi=300, bbox_inches='tight')
plt.show()
print("Data maturity visualisation saved as 'data_maturity.png'")

# ----------------------------------------------------------------
# PART B: DATA STRATEGY ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Data Strategy Roadmap")
print("-"*60)

data_roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Build data governance and quality foundation.",
        "Activities": [
            "Establish data governance framework.",
            "Define data quality standards.",
            "Implement data lineage tracking.",
            "Build data quality monitoring."
        ],
        "Success Metrics": ["Data quality score > 85%", "Data governance framework approved"]
    },
    "Phase 2 (6-12 months) – Integration": {
        "Focus": "Integrate data across the organisation.",
        "Activities": [
            "Build enterprise data platform.",
            "Integrate data sources.",
            "Create a 360° customer view.",
            "Enable self-service analytics."
        ],
        "Success Metrics": ["80% of data sources integrated", "Self-service adoption > 50%"]
    },
    "Phase 3 (12-24 months) – Analytics": {
        "Focus": "Scale analytics and AI capabilities.",
        "Activities": [
            "Build AI/ML capabilities.",
            "Implement predictive analytics.",
            "Scale data science initiatives.",
            "Enable real-time analytics."
        ],
        "Success Metrics": ["10+ AI models in production", "Data-driven decisions > 70%"]
    },
    "Phase 4 (24+ months) – Innovation": {
        "Focus": "Drive innovation with data.",
        "Activities": [
            "Enable data monetisation.",
            "Explore generative AI.",
            "Build an innovation lab.",
            "Foster a data-native culture."
        ],
        "Success Metrics": ["Data monetisation revenue > $10M", "Data literacy > 80%"]
    }
}

for phase, details in data_roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART C: DATA GOVERNANCE FRAMEWORK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Data Governance Framework")
print("-"*60)

governance_framework = {
    "Data Ownership": {
        "Description": "Assign accountability for data.",
        "Roles": ["Data Owner", "Data Steward", "Data Custodian"],
        "Responsibilities": [
            "Define data standards",
            "Ensure data quality",
            "Manage data access"
        ]
    },
    "Data Policies": {
        "Description": "Rules for data management.",
        "Policies": [
            "Data Privacy Policy",
            "Data Retention Policy",
            "Data Access Policy",
            "Data Sharing Policy"
        ]
    },
    "Data Quality": {
        "Description": "Ensure data is fit for purpose.",
        "Dimensions": ["Accuracy", "Completeness", "Consistency", "Timeliness", "Validity"],
        "Metrics": ["Error rate", "Completeness rate", "Timeliness rate"]
    },
    "Data Lineage": {
        "Description": "Track data from source to consumption.",
        "Elements": ["Data sources", "Transformations", "Data flows", "Data consumers"]
    },
    "Data Security": {
        "Description": "Protect data from unauthorised access.",
        "Controls": ["Encryption", "Access controls", "Audit logs", "Data masking"]
    },
    "Compliance": {
        "Description": "Meet regulatory requirements.",
        "Regulations": ["GDPR", "CCPA", "BCBS 239", "PCI DSS"]
    }
}

print("Data Governance Framework:")
for component, details in governance_framework.items():
    print(f"\n{component}:")
    print(f"  {details['Description']}")
    if 'Roles' in details:
        print(f"  Roles: {', '.join(details['Roles'])}")
    if 'Policies' in details:
        print("  Policies:")
        for policy in details['Policies']:
            print(f"    • {policy}")
    if 'Dimensions' in details:
        print(f"  Dimensions: {', '.join(details['Dimensions'])}")
    if 'Controls' in details:
        print("  Controls:")
        for control in details['Controls']:
            print(f"    • {control}")
    if 'Regulations' in details:
        print("  Regulations:")
        for reg in details['Regulations']:
            print(f"    • {reg}")

# ----------------------------------------------------------------
# PART D: DATA QUALITY DASHBOARD
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Data Quality Dashboard")
print("-"*60)

quality_metrics = pd.DataFrame({
    'Dataset': [
        'Customer Data',
        'Transaction Data',
        'Account Data',
        'Loan Data',
        'Product Data',
        'Employee Data'
    ],
    'Completeness (%)': [92, 88, 85, 78, 82, 90],
    'Accuracy (%)': [95, 92, 90, 85, 88, 94],
    'Consistency (%)': [88, 85, 82, 80, 85, 90],
    'Timeliness (%)': [90, 95, 88, 82, 80, 92],
    'Overall Score (%)': [91.25, 90.00, 86.25, 81.25, 83.75, 91.50]
})

print("Data Quality Dashboard:")
print(quality_metrics.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(12, 6))
quality_metrics.set_index('Dataset')[['Completeness (%)', 'Accuracy (%)', 'Consistency (%)', 'Timeliness (%)']].plot(kind='bar', ax=ax)
ax.set_ylabel('Score (%)')
ax.set_title('Data Quality Metrics by Dataset')
ax.axhline(y=90, color='green', linestyle='--', label='Target (90%)')
ax.legend(loc='best')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('data_quality.png', dpi=300, bbox_inches='tight')
plt.show()
print("Data quality visualisation saved as 'data_quality.png'")

# ----------------------------------------------------------------
# PART E: DATA TEAM STRUCTURE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Data Team Structure")
print("-"*60)

team_structure = {
    "Chief Data Officer (CDO)": {
        "Description": "Executive responsible for data strategy.",
        "Reports": ["Head of Data Governance", "Head of Data Engineering", "Head of Analytics"]
    },
    "Head of Data Governance": {
        "Description": "Data governance and quality.",
        "Team": ["Data Stewards", "Data Quality Analysts", "Compliance Specialists"]
    },
    "Head of Data Engineering": {
        "Description": "Data infrastructure and pipelines.",
        "Team": ["Data Architects", "Data Engineers", "Database Administrators"]
    },
    "Head of Analytics": {
        "Description": "Analytics and insights.",
        "Team": ["Data Scientists", "Data Analysts", "BI Developers", "ML Engineers"]
    },
    "Data Science Team": {
        "Description": "Advanced analytics and AI.",
        "Specialists": ["NLP", "Computer Vision", "Risk Analytics", "Fraud Analytics", "Marketing Analytics"]
    }
}

print("Data Team Structure:")
for role, details in team_structure.items():
    print(f"\n{role}:")
    print(f"  {details['Description']}")
    if 'Reports' in details:
        print(f"  Reports: {', '.join(details['Reports'])}")
    if 'Team' in details:
        print(f"  Team: {', '.join(details['Team'])}")
    if 'Specialists' in details:
        print(f"  Specialists: {', '.join(details['Specialists'])}")

# ----------------------------------------------------------------
# PART F: DATA STRATEGY METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Data Strategy Metrics")
print("-"*60)

strategy_metrics = pd.DataFrame({
    'Metric': [
        'Data Quality Score',
        'Data Governance Compliance',
        'Data Literacy Rate',
        'Data-Driven Decisions',
        'Analytics Adoption',
        'Data Monetisation Revenue',
        'Data Incident Rate',
        'Time-to-Insight'
    ],
    'Current Value': [
        '82%',
        '75%',
        '45%',
        '55%',
        '40%',
        '$2.5M',
        '12/month',
        '4.5 days'
    ],
    'Target Value': [
        '> 95%',
        '> 95%',
        '> 80%',
        '> 80%',
        '> 70%',
        '$15M',
        '< 2/month',
        '< 1 day'
    ],
    'Status': ['🟡', '🟡', '🔴', '🟡', '🔴', '🟡', '🟡', '🔴']
})

print("Data Strategy Metrics:")
print(strategy_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Summary and Recommendations")
print("="*70)

print("""
Data-Driven Banking – Key Takeaways:

1. Data-driven banking is essential for digital transformation.
2. Key pillars: strategy, culture, data, technology, talent.
3. Data governance ensures data quality, security, and compliance.
4. Data quality dimensions: accuracy, completeness, consistency, timeliness.
5. Data culture requires executive sponsorship, literacy, and self-service.
6. Data strategy roadmap: foundation → integration → analytics → innovation.
7. Key metrics: quality, governance, literacy, analytics adoption, monetisation.

Recommendations:
  - Assess data maturity and develop a roadmap.
  - Establish data governance framework.
  - Invest in data quality and integration.
  - Build data literacy across the organisation.
  - Foster a data-driven culture.
  - Measure and track data strategy metrics.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 4")
print("="*70)