IT/Data
Live data in nine days.
Aurélien
The retailer
An 11-store outdoor retailer with no IT capacity and a tight calendar.
Our retailer operates 11 stores across France in the outdoor and mountain technical segment. The team is small but sharp: a 2-person buying team with deep category expertise, running roughly 5,000 SKUs in season.
The stack is mainstream for the sector — Ginkoia as core retail management, Shopify for e-commerce, and Excel as the connective tissue between everything else. It works. Until it doesn't.
"We didn't have an IT team. We had me, two buyers, and a tight calendar."
— Operations Lead
Context
An 11-store retailer with no time to wait for IT.
Spring 2025. The outdoor retailer had just decided to roll out Solya across the network. The buying team needed live sell-through analysis before the FW season started — eight weeks away.
The operations lead knew the typical SaaS playbook: kick-off, data mapping workshop, IT ticket, consultant interventions, three months later data starts flowing. She didn't have three months. She had nine days before the first FW review meeting.
The Problematic
Most B2B platforms take quarters. The team had days.
The previous tools the retailer had evaluated all came with the same playbook: a data engineer from the vendor side, a data engineer needed on the client side, a six-week mapping project, custom connectors, then UAT.
The outdoor retailer had no data engineer. No internal IT capacity. No budget for consultants. They had a small team that needed answers fast — and a vendor whose typical onboarding ran longer than the season they wanted to influence.
Three blockers stacked up:
Standard B2B onboarding required IT capacity the retailer didn't have.
Schema mapping workshops would burn weeks of senior time.
Season started in 8 weeks, leaving no margin for delays.
The team needed live data. Not a roadmap to live data.
The Solution
Auto-configured ingestion. Schema detection by Solya. No IT required.
Solya's onboarding doesn't start with a workshop — it starts with credentials. The operations lead provided access to Ginkoia, Shopify, and the warehouse system. Solya's connectors auto-detected schemas, mapped fields to the canonical retail model, and surfaced any ambiguities for a 30-minute review.
Day 3: data flowing into the staging environment. Day 6: first dashboards live. Day 9: production environment with two years of historical data ready for analysis. The buying team made it to the FW review with live data in hand.
Credentials in, day 1 — no data export, no manual prep.
Schemas detected automatically, ambiguities surfaced for review.
2 years backfilled in parallel with live streaming.
Onboarding stopped being a project. It became a setup.
How we did it
Inside the loop.
The nine-day onboarding ran on the Data Layer, with Solya doing most of the configuration work autonomously. Here's how the system works, end to end.
01 — Provide credentials, day 1.
The operations lead gave Solya access to Ginkoia, Shopify, and the warehouse system. No data export, no manual prep.
02 — Auto-detect schemas, day 1-2.
Solya analyzed each system's data structure, identified products, transactions, stock, and customer entities, and mapped them to the canonical model.
03 — Surface ambiguities, day 3.
Where mapping wasn't 100% clear, Solya raised questions. The operations lead resolved them in a single 30-minute call.
04 — Stream data, day 4-6.
Live data flowed from each system into Solya's staging environment. Two years of history was backfilled in parallel.
05 — Go live, day 7-9.
Solya validated data integrity, ran consistency checks, and opened production access. The buying team had live dashboards on day 9.
Onboarding stopped being a project. It became a setup.
The Impacts
Live data before the season started.
Solya's auto-configured ingestion compressed what would have been a quarter-long project into a nine-day setup. The team made the FW review with two years of historical data ready to query.
9 days — From contract to live data.
0 — IT tickets, 0 consultants.
2 years — Of historical data backfilled.
30 min — Of human review needed for schema mapping.
"We onboarded a retail platform in less time than we usually take to onboard an Excel template."
— Operations Lead
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