Case note
Financial Services
Automated Data Pipeline: From 40 Hours to 2 Hours Weekly
Transformed a manual data collection process into an automated pipeline, saving 38 hours of analyst time per week.
Problem
A financial services firm was manually compiling market data from 12 different sources every week. The process took two analysts 20 hours each—pulling data from APIs, cleaning inconsistencies, merging datasets, and generating reports.
Errors were common. Reports were often delayed. And the analysts were frustrated spending their time on data wrangling instead of actual analysis.
Approach
Discovery Phase:
Mapped the existing workflow: which sources, what transformations, what validation rules. Documented every edge case and data quality issue they typically encountered.
Pipeline Development:
Built a Python-based ETL pipeline that automated data collection from all 12 sources, handled API rate limits, performed data validation, and generated standardized reports.
Quality Assurance:
Ran the automated pipeline in parallel with manual process for 4 weeks to verify accuracy. Made adjustments based on discrepancies found.
Outcome
Impact:
- Reduced weekly data collection from 40 hours to 2 hours (95% reduction)
- Eliminated manual data errors—automated validation catches issues before they propagate
- Reports now generated by Monday 9 AM instead of Wednesday afternoon
- Analysts freed up to focus on actual market analysis and insights
- System handles data quality issues automatically with clear alerts for exceptions
The firm now publishes market insights 48 hours faster than competitors and reallocated $200K in analyst time to higher-value work.