Business intelligence

Business intelligence is the ground that optimization and AI work stands on. If data is not clean, consistent and reachable, no model built on it is trustworthy.

Traceability first

The first rule in what we build: it must be possible to see where a number on a dashboard came from, which transformations it passed through, and when it was last refreshed.

There is a practical reason. When a figure in a report is challenged — and it will be — answering “that is how the system calculates it” ends the trust. If the number can be traced, the argument closes in five minutes.

A layered structure

Keeping data in one place and writing reports on top of it is fast in the short term and expensive later. What we build runs in layers.

The raw layer holds what arrives from the source system, untouched. This is where you go back to when something is wrong.

The transformation layer applies business rules: unit conversions, hierarchy mappings, de-duplication.

The serving layer is what reports and models read. Speed matters here, not complexity.

Validation is its own job

The most commonly skipped part of a data pipeline is validation. In the Çimsa fuel optimization project, dozens of checks ran before the model: is capacity defined for every supplier–fuel pair, is warehouse capacity set for every location, is the minimum order quantity smaller than the lot size?

Without these checks the model either fails outright or silently produces a wrong answer. The second is more dangerous, because nobody notices.

What we deliver

Data warehouse design, ETL processes, dashboards and ad-hoc analysis. The number of dashboards we have built passed 120; most of them exist to monitor the output of an optimization or forecasting model.