Case Study
ETL and Data Warehouse Development
Consolidating data from multiple sources into reliable datasets for reporting.
Business Context
Business information was distributed across different systems and file sources. Reporting teams needed a more reliable way to extract, transform and consolidate data into datasets that could be used by reporting platforms.
Key Challenges
- Understand source ownership, formats, timing and data quality
- Transform different structures into consistent business definitions
- Detect missing, duplicate or invalid records
- Operate scheduled processing with monitoring and recovery
KCSG Approach
- Defined source-to-target mappings and validation rules
- Developed ETL tasks for CSV parsing and database integration
- Built consolidated data structures for reporting use
- Included scheduling, logging, reconciliation and error handling
Business Value
The project created a more unified data foundation, improving accessibility and visibility for reporting and operational analysis.
Technology references reflect documented project experience and should be reviewed periodically as supported platforms change.