ADR-2023-12: Modern Data Platform Strategy for FinSecure

Status

Approved (2023-12-18)

Context

FinSecure is experiencing significant challenges with our current data architecture:

1. Data Silos: Customer, transaction, and risk data are siloed across multiple legacy systems with inconsistent data models.

2. Limited Analytics Capabilities: Rigid data warehousing solutions limit our ability to perform advanced analytics and machine learning.

3. Scalability Constraints: Current data processing infrastructure is struggling to handle increasing data volumes (now exceeding 5TB daily).

4. Compliance Complexity: Meeting GDPR, CCPA, and financial regulatory requirements across fragmented data systems is increasingly difficult.

5. Slow Time-to-Insight: Business teams wait 2-3 weeks for new analytics dashboards or data models to be developed.

6. Technical Debt: Legacy ETL processes are complex, brittle, and expensive to maintain.

7. Limited Real-time Capabilities: Current architecture is primarily batch-oriented with limited ability to process streaming data for fraud detection and real-time decisioning.

8. Data Quality Issues: Inconsistent data quality across systems impacts business decisions and customer experience.

These challenges are limiting our ability to leverage data as a strategic asset and inhibiting our digital transformation initiatives aimed at enhancing customer experiences and operational efficiency.

Decision

We will implement a modern, cloud-based data platform with a lakehouse architecture. Key components include:

1. Data Lake Foundation:

   • Azure Data Lake Storage Gen2 as the foundation for our data lake
   • Databricks Delta Lake for ACID transactions and data reliability
   • Structured organization with bronze (raw), silver (refined), and gold (business) layers

2. Data Ingestion and Processing:

   • Azure Data Factory for orchestration and batch data movement
   • Kafka and Azure Event Hubs for real-time data ingestion
   • Databricks for large-scale data processing
   • Stream processing with Spark Structured Streaming

3. Semantic Layer and Data Serving:

   • Databricks SQL Warehouses for analytics workloads
   • Azure Synapse Analytics for enterprise data warehousing needs
   • Power BI as primary business intelligence tool
   • REST APIs for serving data to applications

4. Data Governance and Security:

   • Azure Purview for data catalog and lineage
   • Column-level encryption for sensitive data
   • Role-based access control aligned with data classification
   • Automated data retention and purging based on policies

5. Machine Learning Platform:

   • MLflow for experiment tracking and model registry
   • Databricks ML for model development and deployment
   • Model monitoring and retraining pipelines
   • Feature store for reusable feature engineering

6. DataOps and Automation:

   • Infrastructure as Code using Terraform
   • CI/CD pipelines for data pipelines and transformations
   • Automated testing for data quality and pipeline integrity
   • Comprehensive monitoring and alerting

Platform Architecture by Domain

Domain	Data Types	Primary Tools	Access Patterns	Special Requirements
Customer 360	Customer profiles, interactions, preferences	Delta Lake, Databricks SQL	Batch analytics, Real-time lookups	GDPR compliance, Entity resolution
Transaction Processing	Payment transactions, transfers, statements	Kafka, Delta Lake, Azure Synapse	Real-time streaming, Batch reporting	PCI-DSS compliance, 7-year retention
Risk Management	Credit scores, market data, exposure calculations	Databricks, Delta Lake, ML models	Batch processing, Model inference	Auditability, Model governance
Fraud Detection	Transaction patterns, behavioral signals	Kafka, Spark Streaming, ML models	Real-time streaming, Low-latency scoring	Sub-second latency, High availability
Regulatory Reporting	Aggregated financial data, compliance metrics	Azure Synapse, Power BI	Scheduled batch, Ad-hoc analysis	Immutability, Approval workflows
Marketing Analytics	Campaign data, customer segments, attribution	Databricks, Delta Lake, Power BI	Interactive queries, ML-based segmentation	Identity resolution, Attribution models

Consequences

Positive

1. Unified Data Access: Single platform for accessing enterprise data with consistent governance.

2. Enhanced Analytical Capabilities: Support for advanced analytics, machine learning, and AI initiatives.

3. Improved Scalability: Cloud-native architecture can scale to handle growing data volumes.

4. Reduced Time-to-Insight: Self-service capabilities and streamlined data pipelines reduce time to deliver insights.

5. Better Data Governance: Centralized data catalog, lineage tracking, and security controls.

6. Real-time Capabilities: Support for both batch and real-time processing using the same platform.

7. Cost Optimization: Pay-for-use cloud model with ability to scale resources as needed.

8. Regulatory Compliance: Improved ability to implement and demonstrate regulatory compliance.

Negative

1. Implementation Complexity: Significant effort required to migrate from legacy systems.

2. Skills Gap: New technologies require reskilling of existing teams.

3. Initial Cost Increase: Short-term investment in new technology and parallel running of systems.

4. Data Migration Challenges: Data quality and mapping issues during migration.

5. Operational Changes: New operational procedures and support models needed.

6. Integration Complexity: Connecting legacy systems to new platform requires careful planning.

7. Organization Change Management: New workflows and responsibilities across business and technical teams.

Mitigation Strategies

1. Phased Implementation Approach:

   • Start with highest-value, least-critical data domains
   • Implement foundational capabilities before complex use cases
   • Run legacy and new systems in parallel during transition
   • Create clear success criteria for each phase

2. Talent and Skill Development:

   • Develop comprehensive training program for existing staff
   • Strategic hiring for key specialized roles
   • Partner with platform vendors for enablement
   • Create centers of excellence for key technologies

3. Modern Data Governance:

   • Establish data governance council with cross-functional representation
   • Define clear data ownership and stewardship model
   • Implement automated data quality monitoring
   • Create comprehensive data classification framework

4. Financial Management:

   • Detailed cloud cost monitoring and optimization
   • Business-aligned chargeback model
   • Clear ROI tracking for data initiatives
   • Regular cost optimization reviews

5. Change Management Program:

   • Executive sponsorship and visible leadership
   • Regular communication and success stories
   • Early involvement of business stakeholders
   • Incentives aligned with adoption goals

Implementation Details

Phase 1: Foundation (Q1-Q2 2024)

1. Establish cloud environment and core infrastructure
2. Implement data lake foundation with initial data domains
3. Deploy data catalog and basic governance tools
4. Migrate first non-critical data workloads
5. Establish DataOps practices and pipelines

Phase 2: Expansion (Q3-Q4 2024)

1. Migrate core analytical workloads to the platform
2. Implement real-time data processing capabilities
3. Deploy self-service analytics for business users
4. Enhance data quality frameworks and monitoring
5. Develop initial ML use cases on the platform

Phase 3: Advanced Capabilities (Q1-Q2 2025)

1. Full enterprise adoption across all data domains
2. Advanced ML capabilities and feature store
3. Comprehensive data governance implementation
4. Legacy system decommissioning
5. Advanced real-time analytics and decisioning

Considered Alternatives

1. Modernize Existing Data Warehouse

Pros: Lower initial disruption, familiar technology, focused scope
Cons: Limited flexibility, higher long-term costs, limited real-time capabilities

This approach would not address our fundamental needs for real-time processing, advanced analytics, and managing unstructured data.

2. Traditional Data Lake Architecture

Pros: Lower cost storage, support for varied data types, scalability
Cons: Complexity in ensuring data quality, limited transactional support, governance challenges

A traditional data lake without the lakehouse capabilities would create significant challenges for data reliability, performance, and governance.

3. Multiple Purpose-Built Systems

Pros: Optimized solutions for specific use cases, potentially best-in-class capabilities
Cons: Increased integration complexity, data duplication, inconsistent governance

This approach would perpetuate our data silo issues and create ongoing integration and consistency challenges.

4. Maintain and Incrementally Improve Current Systems

Pros: Minimal disruption, lower initial investment, familiar technology
Cons: Perpetuates technical debt, limited capability improvement, increasing maintenance costs

This would fail to address our fundamental challenges and put us at a competitive disadvantage as data volumes and complexity increase.

References

1. “Designing Data-Intensive Applications” by Martin Kleppmann
2. Databricks Lakehouse Platform Documentation
3. Azure Data Factory Documentation
4. “Data Mesh: Delivering Data-Driven Value at Scale” by Zhamak Dehghani
5. FinSecure Internal Report: “Data Platform Requirements Analysis” (October 2023)
6. DAMA Data Management Body of Knowledge

Decision Record History

Date	Version	Description	Author
2023-10-15	0.1	Initial draft	Jennifer Wu, Chief Data Officer
2023-11-08	0.2	Updated based on technical review	Raj Patel, Data Engineering Lead
2023-12-02	0.3	Added implementation phases and cost estimates	Michael Torres, Enterprise Architect
2023-12-18	1.0	Approved by Executive Technology Committee	FinSecure ETC

Appendix A: Data Platform Architecture

Appendix B: Data Platform Implementation Timeline

Appendix C: Target State Data Flow - Customer 360 Example

Appendix D: Key Performance Indicators

KPI	Current State	Target (2025)	Measurement Method
Data Integration Cycle Time	7-14 days	<24 hours	Average time from source change to data availability
Self-service BI Adoption	15% of business users	>60% of business users	Monthly active users in self-service tools
Data Quality Score	~75%	>95%	Composite score from automated quality checks
Cost per TB of Analytics Storage	$2,500/TB	<$500/TB	Total cost of ownership / storage volume
Time to New Analytics	2-3 weeks	<3 days	Time from request to dashboard availability
Data Platform Availability	99.5%	99.95%	Measured service uptime
Regulatory Report Production Time	10-15 days	1-2 days	Time to produce monthly regulatory reports
Real-time Decision Latency	Not available	<250ms	Response time for real-time decision APIs
ML Model Deployment Time	4-6 weeks	<1 week	Time from model approval to production deployment
Data Engineer Productivity	~30% on new features	>70% on new features	Time allocation analysis

Note: Metrics will be tracked quarterly and reported to the Data Governance Council.