One dashboard, built to become many

The challenge

The first client's problem was familiar: revenue, conversions, and ad performance scattered across GA4, Shopify, and ad platforms that never talked to each other, with nobody able to say confidently how ad spend was actually performing. Solving that once wasn't the real goal, though. Syatt works with many ecommerce clients, so the harder question was what happens next. Does every new client mean weeks of new engineering, or can the same setup extend to them without starting over?

What we did

Datastarter built the platform as a set of shared components: reusable data models for GA4, Shopify, ad platforms, and more, deployed through the same pipeline for every client. Bringing on a new client means writing a small set of configuration, like which data sources and which environment, rather than building a new pipeline from scratch. The same automated deployment process that stood up the first dashboard picks up the next client's configuration and deploys their dashboard the same way, all inside Syatt's own infrastructure.

How it works

flowchart LR
    Shared["Shared pipeline\n(GA4 / Shopify / Ads models)"] --> CI["Automated deploy\n(GitHub Actions)"]
    ConfigA["Client A config"] --> CI
    ConfigB["Client B config"] --> CI
    ConfigN["New client config"] --> CI
    CI --> DashA["Client A dashboard"]
    CI --> DashB["Client B dashboard"]
    CI --> DashN["New client dashboard"]
      

The pipeline itself doesn't change from client to client. Adding one just means adding their configuration and letting the same process pick it up.

The result

• Built once, reused for every client: the transformations, orchestration, and dashboard template are shared, not rebuilt per engagement.
• Scaling to a new client is a configuration change, not a new project, deployed automatically by the same pipeline that runs everyone else's.
• Installed in Syatt's own infrastructure, a system their team owns and can inspect rather than a black box hosted elsewhere.
• Refreshes hourly, automatically, with zero manual work to keep it current.
• Run lean, with near-zero infrastructure cost per client.

For Syatt, that's the shift: what started as one dashboard for one client is now infrastructure they can extend to as many clients as they want, without doing the engineering work over again each time.