Financial Analysis Dashboard

My role

BI Consultant & Data Analyst — end-to-end from raw server data to a live, client-hosted reporting suite

The challenge

A multi-line consumer goods business spanning sports equipment, sportswear and nutrition supplements had no consolidated way to see how the company was actually performing. Finance rebuilt P&L summaries manually every month, ratios were calculated inconsistently, and nobody outside the finance team could self-serve an answer to something as basic as "what's driving our margin this quarter?"

The data problem

Financial data lived across the client's internal servers, with each business line maintaining its own records independently — different formatting, column naming and level of detail. There was no shared chart of accounts, no separation between COGS, operating expenses and interest/tax, and no calculated ratios or trend logic anywhere.

My approach

I built a Data Factory pipeline in Microsoft Fabric to pull the business-line records off the client's servers on a schedule, landing raw files in a Fabric Lakehouse as a central staging point. From there I used Dataflows Gen2 and T-SQL in the Fabric Warehouse to do the real structuring work: standardizing inconsistent naming and formats across business lines, resolving categories labelled differently across sources, and mapping every transaction to a consistent chart of accounts — COGS, Opex, and Interest and Tax — along with subcategories like Labor, Materials, Shipping and Marketing.

With clean, structured tables in the Warehouse I built the semantic model directly on top: category and business-line dimensions linked to the transaction fact table, with a drill-down hierarchy from category to subcategory. Power BI connected natively to the Warehouse, where I built a full DAX layer calculating Gross Profit, EBIT, Net Profit and their margins, plus a dynamic Opex ratio and month-over-month change measures that update automatically as new data lands.

I also modeled a break-even calculation against actual net profit, giving leadership a direct read on how close the business was running to its break-even threshold each month. The report is structured around how different users actually work: an Overview page for a fast executive read, dedicated Revenue and Expenses pages for category-level drill-down, a Ratios page for trend tracking, a raw Table page for finance to audit exact figures, and a Glossary page so non-finance stakeholders understand how each metric is calculated.

The result

Leadership got a live financial reporting tool that replaced the manual monthly rebuild entirely, refreshing automatically end to end from source servers to report. The dashboard surfaced that the Opex ratio spiked to 41.2% in September against a yearly average in the low 30s — a swing that would have taken real digging to catch across disconnected records. It also gave non-finance stakeholders a self-serve way to understand margin performance, cutting down back-and-forth requests to the finance team.

Tools & tech stack

• Microsoft Fabric Data FactoryScheduled pipeline extracting business-line records directly from the client's servers
• Fabric LakehouseStaging layer for raw data before transformation
• Dataflows Gen2 & T-SQLStandardizing formatting and mapping scattered data into a consistent chart of accounts
• Fabric Data WarehouseHosts the modeled star schema — fact table linked to category and business-line dimensions
• Power BI & DAXSemantic model, financial metric calculations, ratio tracking and break-even modeling
• DocumentationIn-report glossary page for non-finance stakeholder self-service