Enterprise Forecasting Transformation

Architecting an automated, driver-based financial planning system for a $500M+ Opex portfolio.

SQL (ETL) Power BI Python Excel Modeling Variance Analysis

The Challenge: Disconnected Data

Managing financial planning for a Central Bank is complex. The organization operated across 16 distinct business units with an annual operating budget exceeding $500M.

The legacy process was highly manual and fragmented:

Data Silos: Financial data lived in disparate legacy banking systems and disconnected spreadsheets.
High Variance: Manual entry errors led to low forecast accuracy and "surprise" variances at month-end.
Slow Cycle Time: Consolidating reports took weeks, leaving leadership with outdated information for decision-making.

The Solution: Automated Data Pipeline

I led the transition from static reporting to a dynamic, driver-based forecasting model. This involved engineering a full data pipeline to automate the flow of information.

1 Ingest & Clean Wrote SQL queries to extract and clean 500k+ transaction records from the core banking system.

2 Model & Transform Built a Star Schema in Power BI to link General Ledger data with Departmental Budgets.

3 Visualize Deployed interactive dashboards for real-time variance tracking and trend analysis.

The Executive Dashboard

The final output was a centralized Power BI dashboard used by the CFO and Department Heads. It provided drill-down capabilities from the Enterprise level down to individual Cost Centers.

Figure 1: Executive Dashboard mock-up showing Budget vs. Actuals tracking.

Business Impact

This transformation shifted the finance function from "data gathering" to "strategic partnership." By automating the manual grunt work, we unlocked significant value:

67% Forecast Accuracy Lift

$5M+ Efficiency Savings

30% Faster Planning Cycle

Key Wins

Cost Control: Identified $5M+ in savings by benchmarking vendor spend across departments.
Agility: Reduced the monthly reporting cycle time by 30%, giving leadership 5 extra days for strategic review.
Compliance: Standardized reporting across 16 units, ensuring 100% audit trail compliance.

Confidentiality Note: Due to the sensitive nature of Central Bank financial data, the actual datasets, SQL code, and dashboards cannot be shared publicly. This page outlines the methodology used and the verifiable business outcomes achieved.

Looking to build similar efficiency?

I specialize in translating raw data into executive decisions. If your team needs someone to bridge the gap between FP&A and Data Science, let's talk.

Schedule a Chat

Back to Home