Derimod business intelligence: 20+ Power BI dashboards fed from one warehouse

20+

Dashboards

One warehouse, one definition

4

Catalogue steps

Reporting → deep analysis → trading → CEO

1

Data sources

No report has its own

Daily

Refresh

Capacity and stock flow live

Problem

If the data warehouse stays somewhere only IT and the analytics team can reach, nothing changes for the rest of the company. At Derimod, weekly meetings began by arguing about whether the numbers were right; every team came from its own Excel and two teams’ “sales” did not agree.

The goal was for everyone who needed the data to reach and read it on their own — and for every figure, whichever dashboard it appeared on, to come from the same table.

A four-step catalogue

We built the dashboards along a catalogue, not at random. Each step stands on the one before; the second does not start until the first is done.

Step	Dashboards	What it shows
1 · Reporting	Sales · Product · Customer · Sales by city	Receipts, units, revenue, cost, average price by year, week, day, product, supplier, channel and store; LFL, MTD, YTD and Pareto by product; customer count, new/repeat, frequency, cohorts
2 · Deep analysis	Basket analysis · Budget/target · Capacity	Confidence, support, lift and the product network; revenue and unit budget, AOV, IPT; last 7 days, overall and shelf fill, store + in-transit stock
3 · Trading	Trading dashboard	Cross views: regional manager × store, supplier × product group, product team × product; tracking by season group
4 · CEO	CEO dashboard	Store and product-group analysis at a glance; a summary of every layer below

Every dashboard’s metric list, pages and audience were written down before it was built; the team knew what it would see when it opened.

Operations dashboards

Alongside the catalogue are dashboards born of operational need:

• Store capacity — total capacity, actual and final stock, option capacity, number of unbroken options, stock in transit, dispatch orders, incoming stock, cover and 28-day cover; separate for apparel and footwear. Stock and capacity data flow live.
• Warehouse capacity — actual, final, available and returns stock; how much stock sits in the warehouse by season and subgroup.
• Goods-in KPIs, dispatched vs waiting in the warehouse, logistics — warehouse in/out, cost and stock pages.
• E-commerce operations, best–worst sellers, supplier performance.
• Optail reports — transfer effectiveness, replenishment success, weekly size-run breaks, idle stock.

The architecture is deliberately simple: all dashboards are fed from the same data warehouse. A sales figure comes from the same table whichever dashboard shows it; the “your report doesn’t match mine” conversation is prevented at the root. We designed the dashboards with the team that needed them — nobody knows what a store capacity report should look like better than store operations.

The third year

The real test of a BI project is not launch day but the third year. When refresh times grew, we found and fixed the slow query instead of raising the timeout, and moved large tables to incremental refresh. Source schema changes are absorbed by the warehouse layer; the dashboards never need to know the source system’s details. Every dashboard has a business owner; the dashboard count does not grow unchecked.

The most tangible difference showed in the weekly meetings: they now start with “what do we do” rather than “are the numbers right”. There was an unexpected effect too: once data was easy to reach, the number of questions grew; part of the catalogue was born from those questions. The set presented to the board in May 2024 — sales, budget, store and warehouse capacity — has been the core set of management briefings since.

Related

• Derimod: from data warehouse to Optail — three layers, seven projects in three years
• Defacto DSO: four years from the founding of a data science office
• Defacto Norm Staffing: calculating the staff each store should have, from data

All our work