This is an illustrative composite: a pattern drawn from several engagements rather than a single booked client. It shows how we approach the problem. Details are anonymized where client confidentiality requires it.
The Challenge
A multi-unit restaurant operator ran each location on its own stack: one system for POS sales, another for labor and scheduling, a third for inventory and food cost, plus accounting and a pile of vendor invoices. Nobody could see store-level profitability until the month closed, and even then the numbers were stitched together by hand in spreadsheets that did not agree branch to branch.
Our Approach
We stood up a client-owned data lake in the operator's own cloud (BigQuery or Microsoft Fabric, in their account, not ours). Connectors landed POS, labor, inventory and food cost, accounting and GL, and vendor invoices into raw zones on a schedule. dbt models cleaned and conformed the data into a shared store dimension and a common chart of accounts, then built a weekly P&L-by-store layer with food and labor variance against theoretical. A benchmarking model ranked locations into owner quartiles, and an ad-hoc query surface let finance answer new questions without waiting on a report build.
The Outcome
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