Pingpod
A full data architecture for a company whose venues run unstaffed, and whose dashboards are resold under its customers' brands.
Context
Pingpod runs table tennis venues that operate with nobody on site. Everything the business knows about itself comes through data: the app, the ads, the Stripe payments. Each of those lived on its own, which made any commercial metric arguable. Two ambitions depended on fixing that: building AI features, and reselling the reporting under other brands.
The problem
- Separate data sources: app, advertising, payments.
- Commercial metrics that could not be calculated reliably.
- An AI roadmap with no consolidated data to feed it.
- A white label offer to serve without rebuilding reporting per customer.
Solution
- Built a full data architecture, from collection to reporting.
- Automated the pipelines with Airflow.
- Set up a data lake on GCP.
- Built Tableau dashboards on that single foundation.
- Produced a white label version of the dashboards for Pingpod's own customers.
Our approach
Scoping
Working out which metrics actually matter to the business before connecting anything.
Collection
Connecting the app, advertising and Stripe sources into the GCP data lake.
Automation
Airflow pipelines so the data arrives on its own, every day.
Reporting
Tableau dashboards, then the white label version for Pingpod's customers.
Results
Data from every source lands on its own in a single lake. Pingpod calculates its commercial metrics on a reliable basis, and resells its dashboards to its own customers.
Start with Compass
In 2 weeks, you know what your data can actually do for you. A structured audit, a prioritised roadmap, before you spend anything else.
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