Data Panda pulls your d2o PMI labour, revenue-forecast and food-purchase data and brings it together with the rest of your hotel or restaurant stack. From one place, we turn it into dashboards, automations, AI workflows and custom apps your general managers, F&B leads and finance teams use every day.
d2o is a Norwegian hospitality software company headquartered in Sandnes, building the PMI platform (Performance Management Intelligence). The product runs across hotel chains, restaurants and contract catering in the Nordics and beyond, with named customers including Strawberry Hotels, Olav Thon Gruppen, Pandox, Radisson and the Odyssey Hotel Group, and reach into Sweden, Germany, France, the UK, the US, South Africa and parts of Asia.
For general managers, F&B directors and finance, PMI is the system of record for the things a PMS or a POS does not own: the rolling revenue forecast per profit centre, the labour schedule that gets built against it, the productivity-per-worked-hour target each department is held to, the food purchases tracked against revenue and the operational P&L that summarises the lot. Modules cover the live forecast across rooms, F&B, breakfast and events, staff management with rotating schedules and split shifts, food purchase management, P&L planning, sustainability tracking on energy and water through GoGreen, and benchmarking against peer properties. The built-in reports cover the day. The harder questions, like why labour cost percentage drifted at one outlet and not another, where productivity-per-hour is sliding ahead of the revenue dip, or which week of next month is heading for an overtime spike, sit between PMI, the PMS and the payroll system. Pulling PMI into a warehouse is how those answers stop being a Friday-afternoon export.
Labour cost, productivity and forecast accuracy joined to the PMS revenue and accounting numbers the rest of the business already trusts.
Turn forecast updates and schedule events into the nudges your GMs and head chefs would otherwise do by hand.
Use forecast history, schedule data and actuals to sharpen the next forecast and catch overtime before it lands.
Small GM, F&B and finance tools that sit on PMI data instead of another export.
A list of concrete reports, automations and AI features we have built on d2o PMI data. Pick the one that matches your situation.
Why is one outlet's labour cost percentage three points worse than the group average?
Labour cost percentage broken down per outlet and per department, with productivity per worked hour and the underlying revenue forecast accuracy next to it. The GM sees whether the gap comes from softer revenue, an overstaffed breakfast shift or a forecast that has been off for three weeks running, instead of getting one number back from PMI without the context.
Which weeks of next month are heading for an overtime spike?
Planned hours per outlet and department against contracted hours, joined to the rolling revenue forecast and historical productivity per hour. Approvers see the two or three weeks where the schedule is already over before payroll closes the period, so the rota gets adjusted on the front end rather than reclassed at the end.
How accurate is our forecast on covers and revenue, and where is it drifting?
Forecast versus actual on covers and revenue per outlet, F&B service and day-of-week, ranked by absolute drift and by lead time. The revenue manager and the F&B director see which forecasters and which days the system gets right, and which ones quietly run twenty percent off and need a different model.
Labour cost percentage and food cost percentage per outlet, against the rolling revenue forecast and the actuals booked in accounting. The monthly P&L review runs on numbers that already line up between PMI, the PMS and the ledger, instead of a reconciliation pass the controller does on a Sunday.
Forecast accuracy on covers and revenue per segment and channel, joined to the actual stays and group bookings the property closed. Group and corporate sales teams see which properties consistently undercall their forecast and which ones overcommit, so the next group quote sits on a believable base.
Productivity per worked hour, schedule-versus-forecast efficiency and overtime exposure on one screen, per outlet and department. The GM and the head of housekeeping argue about the same numbers on Monday morning, not about whose PMI export is the right one.
PMI labour cost, productivity per hour and forecast accuracy join the revenue Mews booked per property and meal period, so labour cost percentage lands next to RevPAR and F&B revenue on the same report. Hotel GMs see where the labour-versus-revenue gap is opening up the same week it happens, instead of waiting for the monthly P&L to point it out.
PMI food-purchase data per outlet is joined to the theoretical food cost Apicbase calculates from recipes and assortment, so the gap between bought and theoretical cost is visible per recipe, outlet and week. F&B directors stop guessing where the leak is and see whether it sits with one supplier price drift or with one chef's portioning.
PMI planned and approved hours per employee, outlet and department are matched against the actual paid hours coming out of SD Worx Cobra, with overtime, sick leave and contract types pulled out separately. Finance closes payroll with a known delta per outlet rather than a controller reading two systems side by side, and HR sees which departments structurally schedule above contract.
PMI labour cost and food-purchase data per outlet and department are posted into Exact Online as the right cost-of-sales lines and accruals, with the variance against the rolling forecast booked separately. The monthly close runs without a manual reclass on hospitality cost lines, and the operational P&L the GM sees in PMI matches the financial P&L the controller sees in Exact.
These are the 19 tables we currently pull from d2o PMI 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 d2o PMI 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.
The connector pulls the live forecast tables (revenue and covers per profit centre and lead time), staff-management data (planned and approved hours per employee, outlet and department), food-purchase records, the operational P&L lines and the productivity targets each department is held to. GoGreen consumption data on energy and water is in scope where the property uses that module. The full PMI back-office, like account-mapping config or audit trail, is not in the standard pull.
PMI is the system of record for the rolling forecast, the schedule built against it and the productivity target each department reports on. The PMS owns the actual reservations and revenue, the POS owns F&B revenue per outlet, and payroll owns the paid hours. The value of pulling PMI into a warehouse is that those four sides can be joined on outlet, date and department, so labour cost percentage, productivity per hour and forecast accuracy sit next to actual revenue and actual paid hours on one report instead of four exports.
Yes. Each property and each profit centre stays as its own dimension on the forecast, schedule, food-purchase and P&L tables. Group-level reporting on labour cost percentage, productivity per hour and forecast accuracy joins them on the shared identifiers PMI assigns, and the benchmarking ranks each property on the same KPI definitions instead of one site's local interpretation.
We review your d2o PMI setup and the systems around it. Together we pick the first thing worth building.