Management Reporting Automation for an International Airline

The management team spent time every day copying and filtering Excel data to produce revenue reports. We first documented the accounting logic behind the reports, then built a data platform with Databricks on AWS so reports update and go out automatically at T+1—laying the data foundation for future AI applications.

Background & challenges

• The management team copied data from the revenue management system into Excel by hand every day—slow, and dependent on specific people.
• Order filtering and cross-day revenue recognition were adjusted manually, making the logic tedious and error-prone.
• Static Excel formulas couldn’t support analysis by route, station or aircraft type, so managers couldn’t drill down to find causes.

Our approach

Following Datas’ four-step method: get the data right first, then deliver the application, then keep it running.

1. Data audit: Reverse-engineered hidden Excel formulas, revenue-recognition logic and stored procedures into a data dictionary and ETL specifications, documenting rules that lived only in a few people’s heads.
2. Build the platform: Built a Bronze / Silver / Gold architecture with Databricks on AWS, loaded more than ten years of history, and replaced Excel VLOOKUPs and pivot tables with a star schema.
3. Reporting: Developed executive dashboards and daily operations reports in Power BI, with filtering and drill-down by station and aircraft type.
4. Operate & optimize: Scheduled daily extraction, calculation and email delivery, so the team no longer touches the reports by hand.

Results

• Reports moved from daily manual work to automatic T+1 refresh and delivery—about 2 hours of daily preparation eliminated, saving roughly 40 hours a month.
• A single source of truth means every department sees the same revenue numbers.
• More than ten years of history now lives on the cloud platform, ready for analysis and drill-down by station and aircraft type.
• The cloud data architecture extends directly to LLM and Copilot applications.

Related services & next steps

• Data Platform & Data Engineering: the core of this project—from logic audit and three-layer architecture to automated pipelines.
• AI Assistant: can run on the same platform so managers can ask about revenue and route performance in one sentence.
• AI Agent Process Automation: can extend to automatic alerts when key figures look abnormal.