Automated Monthly Reporting Pipeline
Case study: how we automated a manual monthly reporting process, reducing cycle time from 3 days to roughly 2 hours while improving consistency and auditability.
Overview
This project involved replacing a largely manual monthly reporting process with an automated pipeline that pulls data from source systems, applies business rules in code, and feeds a Power BI dataset. The result was a dramatic reduction in cycle time and a more consistent, traceable process.
Problem
The finance team spent roughly three days each month collecting data from multiple systems, reconciling and transforming it in spreadsheets, and then building and refreshing reports. The process was error-prone, hard to audit, and left little time for analysis or variance commentary.
Approach
We designed a pipeline that:
- Pulls data from the relevant databases and exports via SQL and approved APIs.
- Runs transformations and checks in Python (with tests) so that logic is versioned and repeatable.
- Outputs a clean dataset that feeds into Power BI for dashboards and management packs.
- Runs on a schedule (e.g., nightly or at month-end) so that by the time the team needs to review, the numbers are already updated.
Tech Stack
- Python — ETL, validation, and orchestration
- SQL — Source data extraction
- Power BI — Visualization and distribution
- Automation — Scheduled jobs (e.g., cron or a workflow engine)
Results
- Cycle time for the main monthly pack dropped from about 3 days to roughly 2 hours of human effort.
- Fewer manual errors and a clear audit trail from source data to report.
- The team could reallocate time to analysis, forecasting, and stakeholder support instead of data wrangling.
Links
This was an internal case study; no public repo or live link. Happy to discuss the approach in a consulting or workshop context.