AutomationCase Study
Financial Data Automation Pipeline
Reduced manual data entry by 80% through intelligent automation
See It In Action
Loom Video Embed Here
<iframe src="https://www.loom.com/embed/YOUR_VIDEO_ID"></iframe>Watch how the automation extracts data from multiple sources, transforms it, and loads it into dashboards in real-time.
The Problem
The finance team was spending 15+ hours per week manually copying data from multiple Excel files, validating entries, and updating Power BI dashboards. This process was:
- Time-consuming and tedious
- Error-prone due to manual data entry
- Delaying critical business decisions
- Preventing team from focusing on analysis
The Solution
Built an end-to-end automation pipeline using Python and Zapier that:
- Extracts: Automatically pulls data from 5 different source systems via APIs
- Transforms: Applies business rules, validates data quality, and handles exceptions
- Loads: Updates Power BI datasets and sends notifications when complete
- Monitors: Tracks errors and sends alerts if data quality thresholds aren't met
Technology Stack
PythonPandasZapierPower BI APIExcelPostgreSQLSlack API
Results & Impact
80%
Reduction in manual entry time
15hrs
Saved per week
95%
Fewer data errors
System Architecture
Architecture Diagram Here
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