The ECommerce Analytics Dashboard represents a comprehensive data analytics solution built on Google Cloud Platform (GCP) for a fintech organization. This project demonstrates how modern cloud technologies can transform raw loan data into actionable business intelligence, enabling data-driven decision making across financial services operations.
Live Demo: https://kkebaara.github.io/DataAnalystPortfolio/index.html
Financial institutions generate massive volumes of loan data across multiple systems and touchpoints. The challenge lies not in data collection, but in transforming this disparate information into coherent, actionable insights that can drive business strategy and risk management decisions.
Our client faced several critical challenges:
Data Storage: Google Cloud Storage for raw data ingestion and BigQuery for structured analytics
Data Processing: Advanced SQL transformations with Create Table As Select (CTAS) optimization
Visualization: Google Looker for interactive dashboard development
Performance Optimization: Materialized views and strategic data partitioning
The solution implements a modern Extract, Transform, Load (ETL) pipeline designed for scalability and real-time analytics:
-- Example CTAS optimization for loan performance analytics
CREATE TABLE `project.dataset.loan_performance_optimized` AS
SELECT
loan_id,
borrower_id,
loan_amount,
interest_rate,
loan_status,
origination_date,
risk_score,
-- Performance metrics calculations
CASE
WHEN payment_history_score >= 750 THEN 'Low Risk'
WHEN payment_history_score >= 650 THEN 'Medium Risk'
ELSE 'High Risk'
END AS risk_category,
-- Time-based aggregations for trend analysis
DATE_TRUNC(origination_date, MONTH) as origination_month,
-- Calculated fields for dashboard KPIs
ROUND(loan_amount * interest_rate / 100, 2) as annual_interest_amount
FROM `project.dataset.raw_loan_data`
WHERE loan_status IS NOT NULL
AND origination_date >= '2020-01-01'
CLUSTER BY loan_status, risk_category;
Challenge: Loan data existed across multiple BigQuery datasets and Cloud Storage buckets with inconsistent schemas.
Solution: Developed a comprehensive data integration layer using advanced SQL transformations:
Challenge: Complex analytical queries taking minutes to execute, hindering interactive dashboard performance.
Solution: Strategic implementation of Create Table As Select (CTAS) optimization:
Performance Impact:
Stakeholder-Centric Design: Created role-specific dashboards tailored to different organizational needs:
Executive Dashboard Features:
Risk Management Dashboard Features:
Operations Dashboard Features:
Cross-Filtering Capabilities: Implemented dynamic filtering allowing users to drill down from portfolio-level metrics to individual loan analysis.
Automated Refresh: Configured scheduled data refreshes ensuring stakeholders always access current information.
Conditional Formatting: Applied visual indicators highlighting critical thresholds and alert conditions:
-- Example conditional logic for dashboard alerts
SELECT
loan_id,
borrower_name,
days_overdue,
CASE
WHEN days_overdue >= 90 THEN 'Critical'
WHEN days_overdue >= 30 THEN 'Warning'
ELSE 'Normal'
END as alert_status,
-- Color coding for dashboard visualization
CASE
WHEN days_overdue >= 90 THEN '#FF4444' -- Red
WHEN days_overdue >= 30 THEN '#FFA500' -- Orange
ELSE '#00AA00' -- Green
END as status_color
FROM loan_monitoring_view
WHERE loan_status = 'Active';
Dimensional Modeling: Implemented star schema architecture optimizing for analytical queries:
Advanced Query Patterns: Leveraged BigQuery’s analytical capabilities for complex business logic:
-- Portfolio risk analysis with window functions
WITH loan_risk_analysis AS (
SELECT
loan_id,
risk_score,
loan_amount,
-- Portfolio context for individual loans
AVG(risk_score) OVER (
PARTITION BY DATE_TRUNC(origination_date, QUARTER)
) as quarterly_avg_risk,
-- Percentile ranking for comparative analysis
PERCENT_RANK() OVER (
ORDER BY risk_score
) as risk_percentile,
-- Running totals for cumulative analysis
SUM(loan_amount) OVER (
ORDER BY origination_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) as cumulative_origination_amount
FROM optimized_loan_data
)
SELECT * FROM loan_risk_analysis
WHERE risk_percentile >= 0.9; -- Top 10% riskiest loans
Caching Strategy: Implemented multi-tier caching for optimal user experience:
Decision-Making Speed: Reduced time from data request to insight delivery from days to minutes.
Stakeholder Adoption: 95% of finance and risk management teams actively using dashboards for daily operations.
Data Accuracy: Eliminated manual reporting errors through automated data pipeline validation.
Key Metrics Delivered:
Risk Management Enhancement: Real-time portfolio monitoring enabling proactive risk mitigation strategies.
Regulatory Compliance: Automated reporting capabilities supporting audit requirements and regulatory submissions.
Market Expansion: Data-driven insights informing geographic and demographic expansion strategies.
Problem: Processing millions of loan records with near real-time requirements.
Solution: Implemented streaming data pipeline with incremental refresh patterns and intelligent data partitioning strategies.
Problem: Financial calculations requiring precision and regulatory compliance.
Solution: Developed comprehensive data validation framework with automated testing and business rule verification.
Problem: Business users unfamiliar with self-service analytics tools.
Solution: Created role-based training programs and intuitive dashboard designs with guided navigation and contextual help.
Cost Optimization: Strategic use of BigQuery slots and storage classes reducing operational costs by 35%.
Security Implementation: Row-level security and field-level encryption ensuring compliance with financial data regulations.
Scalability Design: Architecture supporting 10x data volume growth without performance degradation.
Stakeholder-Centric Design: Dashboards tailored to specific business roles and decision-making processes.
Progressive Disclosure: Information hierarchy allowing users to drill from summary to detail level analysis.
Performance Optimization: Efficient chart rendering and data aggregation for responsive user experience.
Machine Learning Pipeline: Integration with Google Cloud AI for predictive risk modeling and automated loan approval optimization.
Real-Time Streaming: Implementation of Cloud Dataflow for real-time loan application processing and instant risk assessment.
Advanced Visualization: Integration with custom JavaScript visualizations for specialized financial analytics.
External Data Integration: Incorporation of credit bureau data, economic indicators, and market trends for enhanced risk modeling.
API Development: RESTful APIs enabling integration with loan origination systems and third-party financial tools.
The ECommerce Analytics Dashboard project demonstrates how modern cloud technologies can transform traditional financial data analysis. By leveraging Google Cloud Platform’s powerful analytics capabilities, we created a solution that not only improves operational efficiency but fundamentally changes how financial institutions approach data-driven decision making.
This project showcases the convergence of technical expertise and business acumen required for successful data analytics implementation in regulated industries. The combination of performance optimization, user-centric design, and robust data architecture creates a foundation for sustained business value and competitive advantage.
The success of this implementation validates the potential for cloud-native analytics to drive digital transformation in financial services, providing a roadmap for organizations seeking to modernize their data infrastructure and analytics capabilities.
This project demonstrates practical application of enterprise-grade data analytics solutions and highlights the importance of balancing technical performance with business usability in financial technology implementations.