Data Panda brings weather feeds 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.
A weather feed exposes a few standard things over a REST API: current conditions, hourly and daily forecasts, historical observations, severe-weather alerts and a list of resolvable locations by lat-long, postcode or city. Most providers cover the same global grid and refresh on a fixed cadence. The choice between them is mostly about coverage depth, history length and licensing terms.
The reason to land weather in a warehouse is that the value is in the join. Temperature next to terrace covers, rainfall next to bike-shop foot traffic, wind speed next to solar yield, a heat alert next to call-centre volume. As a standalone dashboard a weather widget tells you what you can see out the window. Joined to your sales, ops and energy data, it tells you how much of the variance you have been calling random is in fact the weather.
Sales, footfall and energy use side by side with the weather they ran in.
Let the next forty-eight hours of forecast move staffing, stock and outreach.
Add weather as a feature to the models that already drive your business.
Internal tools that put the next weather window next to the operational decision.
A list of concrete reports, automations and AI features we have built on Weather API data. Pick the one that matches your situation.
How much of last quarter's revenue swing was the weather?
Daily sales per site joined to local temperature and rainfall, with a year-on-year compare on like-for-like weather days. Splits the part of the variance that the weather explains from the part that store performance, marketing or pricing has to answer for.
Is our staffing keeping up with the weather?
Forty-eight-hour forecast joined to historical traffic-per-weather-pattern, per site. Surfaces the days where the roster is set for an average week and the forecast is anything but, before the queue at the till makes the same point.
Does adding weather to the demand model move the forecast?
A side-by-side run of the current demand forecast and the same model with temperature, rain and wind as added features. Shows the SKUs and sites where the weather features earn their place, and the ones where they only add noise to a model that was already accurate.
Like-for-like sales adjusted for the weather that day, so quarter reviews stop blaming or rewarding the wrong things. Energy-cost forecasts that move with the temperature curve instead of last year's average.
Site managers and merchandisers see the next forty-eight hours of forecast next to expected demand per category. Promotions and stock moves are timed to the weather window, not to a calendar block.
Field service, hospitality and retail rosters react to severe-weather alerts before the day starts. The same forecast feeds energy, scheduling and inventory, so the operations side stops solving the same weather question three times.
Daily Shopify orders per store land next to the local temperature, rainfall and wind for that day. Year-on-year reads stop swinging on a hot week or a wet weekend, and the categories where weather moves the basket get their own forecast inputs.
Invoiced revenue per period and per site in Exact Online sits next to the temperature and rainfall pattern from the same period. Month-end commentary moves from generic explanations to a measured statement about the share of the variance the weather drove.
HubSpot lists for outdoor-services prospects, terrace-equipment buyers or seasonal campaigns trigger on the upcoming forecast in the contact's region. Marketing reaches buyers in the week the weather makes them ready to buy, instead of on a fixed campaign calendar.
Severe-weather alerts for a region create or update items on the right monday.com board, with the affected sites and routes pre-filled. Project leads see the operational impact in the tool they already use, instead of in a separate weather inbox.
A morning Slack post per site channel summarises the temperature, rainfall and any alerts for the next twenty-four hours, with the sales-impact estimate from the model. Ops, retail and field-service leads start the day on the same forecast.
Deputy shift recommendations per site are nudged on the forecast for that day, using the historical relationship between weather and traffic. Hot-weekend terraces get the staffing they need before the queue forms, and quiet rainy days do not get over-rostered.
You keep the reporting tool you already have. We connect it to the warehouse where your Weather API 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.
We review your Weather API setup and the systems around it. Together we pick the first thing worth building.