The Problem
Several business units relied on manual data entry to move information between systems — exporting from one platform, reformatting, and re-importing into another. This created bottlenecks, introduced transcription errors, and consumed staff time that could be redirected to higher-value work.
The Approach
- Mapped all affected workflows using BPMN to understand volume, frequency, and error rates
- Prioritized automation candidates by ROI: high-frequency, low-complexity tasks first
- Deployed N8N for API-based automation between platforms
- Built Python scripts for data validation and transformation at pipeline boundaries
- Established monitoring dashboards to track automation health and error rates
The Outcome
- 40% reduction in manual data entry
- ~200 staff hours per month redirected to analysis and strategy
- Error rate in downstream reporting significantly reduced