Data Panda brings your Square 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.
Square is the SMB operating system for businesses that take a card across a counter. The same Square account holds the Reader or Terminal that swipes the card, the Square POS app on the iPad, the Square for Restaurants or Square for Retail vertical, the Square Online webshop, and Square Banking with Checking, Savings, Loans and a debit card. Payroll, Appointments, Marketing, Loyalty, Gift Cards and Invoices live next to it.
Inside Square you have payments, orders, items, modifiers, categories, inventory counts, customers, employees, locations, payouts, refunds and disputes. That picture is detailed for one shop, but the questions a multi-location owner, a coffee chain or a salon group needs to answer cross Square and the accounting package, the reservations app and the bank statement. Pulling Square into a warehouse is where same-store growth, blended card cost and tip accounting become numbers your team agrees on.
Same-store growth, blended card cost and labour share on one timeline instead of three Square dashboards.
Turn till data, payouts and Square Banking flows into bookings the rest of the stack reacts to.
Use sales history, weather and labour data together to sharpen the rota and the menu.
Manager-facing tools that live on top of Square data, not next to it.
A list of concrete reports, automations and AI features we have built on Square data. Pick the one that matches your situation.
What does a card transaction cost us once everything is netted out?
Gross sales minus Square processing fees, refund fees and chargeback losses, divided by net sales, per location and per card brand. Tells the owner whether the cash-discount programme on the door is paying for itself or just annoying customers.
Are tips landing where they should after the shift closes?
Pooled tip totals per shift joined to clock-in records and to the payroll run, so the share each role gets reconciles end-to-end. Catches the missed entry, the manual override and the staff member whose tip totals look off compared to their hours.
Is the Square Loans repayment faster or slower than the bank line of credit it replaced?
Daily payout deductions against the original Square Loans balance, projected to a payoff date, next to what an interest-only credit line would have cost over the same period. The owner sees whether the percentage-of-sales repayment is helping cash flow or eating it.
Daily Square payouts posted as bank journals with Square fees split out, against the actual deposit on the account. The blended card cost per location is a number on the dashboard, not a calculation in a spreadsheet on the fifth of the month.
Item mix, modifier attach rate and tip totals per server show up next to schedules, so the manager spots the upsell that works on Friday but not on Tuesday. Loyalty redemptions tie back to the customer record on the floor.
Labour as a share of net sales per shift, against forecasted demand per daypart, drives the next rota. Refund and dispute patterns per cashier and per item flag training opportunities before they cost a payout.
Daily Square payouts land in QuickBooks Online as bank journal entries, with Square fees, refunds and tip pass-throughs split into the right accounts. The deposit on the bank statement matches the QuickBooks line on the day it arrives, so monthly close stops with a multi-tab spreadsheet between Square and the books.
Customer purchase history from Square POS and Square Online flows into Mailchimp as profile fields and events. A first-visit buyer and a thirty-day-lapsed regular get different journeys, instead of every email leaving as one campaign blast that ignores what people bought.
For sellers who run Square at the counter and Shopify online, Data Panda stitches the same customer, item and order across both. Stock counts, refunds and lifetime value reconcile at group level, so the Shopify report and the Square report stop telling two different stories about the same buyer.
Net sales, transaction count, average ticket and tip pool per location post to a Slack channel right after closing, with a flag if a number drifts more than ten percent against the four-week trend. The owner sees the day on the way home, instead of opening the merchant dashboard at eleven at night.
You keep the reporting tool you already have. We connect it to the warehouse where your Square 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.
Each Square location lands with its own identifier, and sales, items, employees and payouts join to the right one. Same-store comparisons, group totals and per-location margin become ordinary queries in the warehouse instead of three separate dashboard exports stitched in Excel.
Yes, where the Square account exposes them through the API. Banking transactions reconcile against the merchant payouts that fund them, Square Loans repayment shows up as the percentage-of-payout deduction it really is, and payroll lines land next to the hours and tip totals they paid out for.
Stripe is online-first, with most volume coming through a checkout API. Square is built around a card reader, a till and a physical seller, with online as one channel among several. The Square connector is shaped for that: locations, registers, employees, modifiers, tips and physical inventory all matter, and the warehouse model treats them as first-class objects.
We review your Square setup and the systems around it. Together we pick the first thing worth building.