Data Warehouse Process ETL

1. Data Collection and Extraction
   • Collects data from multiple sources
   • Transactional systems, customer data, market and 3rd party sources, regulatory and compliance
2. Data Transformation and Cleansing
   • Data standardization
   • Data validation – cleans up incorrect duplicate and incomplete records
   • Aggregation to combine data
3. Data Loading and storage
   • Transformed data goes into data warehouse
   • Stored in structured formats
   • Updated periodically
4. Data Analysis and reporting
   • BI – business intelligence tools to analyze reporting AML and other regulations
   • Risk management
   • Regulatory reporting
5. Data Access and Utilization
   • Stakeholder reviews
   • Dashboards, KPIs, ML to help make decisions
   • Monitoring improves fraud detection
6. Data Governance and Security
   • Strict data security – encryption and access control
   • Compliance with regulations
   • Data lineage and auditing track changes

What are Data Tools Used For?

• Data integration, extraction, storage
• Business intelligence

1. ETL – extract from various sources, clean and transform it, put in data warehouse
2. Data warehouse and storage – store large amount of values of data
3. Data analytics and business intelligence (BI) tools
4. Big data processing machine learning
5. Data governance and security tools