Data Panda brings your Midelco data together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
Midelco is the kassa-software that Dirmacom (Puurs) has been building since 1987 for Belgian fashion boutiques and shoe retailers. It is the system behind a hundred-plus shops across Flanders: chains like Manexco, Van Loock, Lenaerts, Delaere, Enoks and others. The product is built around the data structure that the sector needs: an article that lives in seasons, brands, maingroups, subgroups, themes, colours and subcolours, with stock kept per shop and per article and a per-shop sales line for every till transaction.
That is a rich operational picture: articles, article links, article matrices, EAN codes, brands, suppliers, shops, customers, card registrations, seasonal pricing, web action codes for the linked webshop. The reports a fashion buyer, a shop manager and the boekhouder need sit on top of that data and on top of accounting, marketing and the webshop. Midelco runs the till and the reservation flow well, but the answers about margin per brand per shop, like-for-like per season and dead-stock by colour live one layer up. Pulling Midelco into a warehouse is where those answers stop being a Dirmacom export plus an Excel.
Margin, stock and sales rolled up the way the sector reads them: brand, season, colour and shop.
Turn till sales and per-shop stock into action in the rest of the stack instead of an end-of-month catch-up.
Use seasons, sizes, colours and customer history to sharpen buying and clienteling.
Buyer and shop-floor tools that replace the export-and-mail-around routine in Midelco.
A list of concrete reports, automations and AI features we have built on Midelco data. Pick the one that matches your situation.
Which brands make money per shop?
Sales lines joined to article cost and current landed cost, rolled up per brand and per shop. Shows which brands carry the boetiek and which ones move volume but leave nothing once the leverancier-factuur is in.
Where is the same article-colour-size selling in one shop and gathering dust in another?
Per-shop stock against per-shop sell-through on the article-colour-size key. Surfaces the SKU that runs in Antwerp and sits in Hasselt, so a transfer goes out instead of a fresh purchase order.
Does the day-end reconcile to the boekhouding without exporting from Midelco?
Workstation totals, payment-type splits and refunds pushed daily into Exact Online or Yuki as journaalposten with the correct VAT split. The bookkeeper opens a clean day, not a tab full of Midelco-exports.
Daily till reconciliation with the boekhouding instead of an end-of-month catch-up. Real margin per brand and per shop after supplier invoices and seasonal markdowns, ready on the first.
Buyers see margin, sell-through and supplier lead time per brand on one screen. Shop managers see today against plan per category before the after-school rush hits.
Inventory drift between shops, dead stock per colour and supplier fill rate surface before they hurt. Transfers replace a second purchase order when the same article-colour-size already sits in another shop.
Workstation totals, payment-type splits and refunds from Midelco land daily in Exact Online as journal entries with the right VAT split per shop. The end-of-day reconciles on its own and the bookkeeper stops chasing Midelco-exports to close the month.
For a fashion or footwear retailer running Midelco in the shop and Shopify online, Data Panda stitches the same customer, article-colour-size and order across both. Sales, returns and loyalty reporting hold up at group level instead of living in two disconnected dashboards.
Card-registration purchases and reservation events from Midelco flow into Klaviyo as profile properties and triggers. Shop customers receive size-and-brand campaigns based on what they bought in the shop, and a flow goes out the moment a reservation is ready for pickup.
Enriched article records from Akeneo flow into Midelco articles, brands, maingroups and colour-size variants, with the descriptions, attributes and media the linked webshop already uses. Staff on the floor see the same product story the customer saw online and catalogue updates stop being a double data-entry job.
These are the 31 tables we currently pull from Midelco into your warehouse. Query them directly in SQL, join them to the rest of your stack, or build reports on top.
Missing a table you need? We can extend the sync. Tell us what is missing and we will build it for you.
You keep the reporting tool you already have. We connect it to the warehouse where your Midelco data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Midelco is built by Dirmacom, based in Puurs, Belgium. They have been writing the product since 1987 specifically for the Belgian fashion and footwear retail market, with more than a hundred shops on it today.
Articles, their colour-size matrices and EAN codes land in the warehouse with the relationships intact, so a parent article and every colour-size SKU sit together. Margin and sell-through reports roll up per brand or per maingroup and drill down to the exact colour-size that is making or losing money.
Each shop is a first-class entity in the model and stock, sales and customers are joined to the right shop. Group reporting, transfers and like-for-like comparisons are ordinary queries in the warehouse instead of a manual merge job.
We review your Midelco setup and the systems around it. Together we pick the first thing worth building.